Hanis-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter Placeholder with learn-env"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem:\n",
    "\n",
    "- Microsoft want to create a new movie studio but have no idea about creating movies\n",
    "- respondible to ecplore what types of films are currently doing the best at the box office\n",
    "- turn analysis to actionable insights\n",
    "- the results from that will help Microsoft's new movie studio decide what type of films to create."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data:\n",
    "\n",
    "- main data to use: IMDB (sql data) - movie_basics and movie_ratings table\n",
    "- bom.movie_gross.csv "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis:\n",
    "\n",
    "- Your analysis should yield three concrete business recommendations.\n",
    "- Communicating about your work well is extremely important.\n",
    "- highlighting the most important points and skipping over the rest.\n",
    "- Use plenty of visualizations.\n",
    "-  three visualizations which correspond to three business recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measurements to consider in measuring a movie's success based on [article](https://www.hollywoodreporter.com/movies/movie-news/what-is-profitable-movie-ever-1269879/)\n",
    "\n",
    "- production cost\n",
    "- box office gross\n",
    "- marketing domestic/international\n",
    "- rentals (the money the studio makes)\n",
    "- distribution fee\n",
    "- theatrical profit \n",
    "- international revenue\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measurements available from data files\n",
    "\n",
    "- domestic gross\n",
    "- foreign gross\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data understanding\n",
    "\n",
    "### IMDB Database\n",
    "\n",
    "* `movie_basics` : Table containing movies including original movie title, premiered movie title(primary title?), release year, length of the movie and the genre\n",
    "* `persons` : Table containing the name of people in the theatre entertainment industry and their primary profession (actor, director etc.)\n",
    "* `directors`: Table with movie ID and its correspongin director's ID\n",
    "* `known_for`: Table containing the person's ID and the corresponding movie ID that the person is known for.\n",
    "* `movie_akas`: Table containing movies and the title they are known as in different countries.\n",
    "* `movie_ratings`: Table containing movie ID and their average rating and number of ratings they received on IMDB\n",
    "* `principals`: Table containing movie ID and the corresponding person ID that is involved in the movie in some sort of capacity, specified in the category, jobs and character (if they are actors) columns.\n",
    "* `writers` : Table containing movie ID and their corresponding writers' person ID\n",
    "\n",
    "### bom.movie_gross.csv\n",
    "* **Source** : This dataset comes from [Box office mojo](https://www.boxofficemojo.com/) - a website that tracks film revenue\n",
    "* **Contents** : Data about movies' domestic and international gross profit along with the name of studio production and release year\n",
    "\n",
    "### rt.movie_info.tsv\n",
    "* **Source** : This dataset comes from [Rotten Tomatoes](https://www.rottentomatoes.com/) - an \"online aggregator of movie and TV show reviews from critics\"\n",
    "* **Contents** : Data about movies including their synopsis, rating, genre, director, writer, release data in theatre, release data on DVD, Box office rental profit, length of movie and studio of production.\n",
    "\n",
    "### rt.reviews.tsv\n",
    "* **Contents** : Data about movies' domestic and international gross profit along with the name of studio production and release year\n",
    "\n",
    "### tmdb.movies.csv\n",
    "* **Contents** : Data about movies' domestic and international gross profit along with the name of studio production and release year\n",
    "\n",
    "### tn.movie_budgets.csv\n",
    "* **Contents** : Data about movies' production budget along with release date, domestic gross and worldwide gross\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we import the `sqlite3`, `pandas` and `numpy`, which will be used to for reading and exploring our data. We are also importing `seaborn` and `matplotlib` modules, which we will use for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Run this cell without changes\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions for our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "strings_to_remove = ['$',',']\n",
    "\n",
    "def remove_chars(ds,char):\n",
    "    \"\"\"\n",
    "    remove characters from the data series\n",
    "    -\n",
    "    ds: data series, lists. Element in data series or list must be a string.\n",
    "    char: character to be removed. Accepts str, lists\n",
    "    \"\"\"\n",
    "    for char in char:\n",
    "        idx = 0\n",
    "        for row in ds:\n",
    "            if char in row:\n",
    "                ds[idx] = row.replace(char,\"\")\n",
    "            idx +=1\n",
    "    return ds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_sql_as_df(table_name, con):\n",
    "    \"\"\"\n",
    "    Selects all columns in a database table and convert it into a Dataframe\n",
    "    -\n",
    "    Input:\n",
    "    table_name = takes in a string of the table name to import\n",
    "    con = variable that has the established database connection   \n",
    "    -\n",
    "    Output:\n",
    "    DataFrame containing all columns of the database table.\n",
    "    \"\"\"\n",
    "    query = \"\"\"SELECT * FROM ({table_name})\"\"\".format(table_name = table_name)\n",
    "    return pd.read_sql(query,con)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMDB Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = sqlite3.connect('./zippedData/im.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>tbl_name</th>\n",
       "      <th>rootpage</th>\n",
       "      <th>sql</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>table</td>\n",
       "      <td>movie_basics</td>\n",
       "      <td>movie_basics</td>\n",
       "      <td>2</td>\n",
       "      <td>CREATE TABLE \"movie_basics\" (\\n\"movie_id\" TEXT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>table</td>\n",
       "      <td>directors</td>\n",
       "      <td>directors</td>\n",
       "      <td>3</td>\n",
       "      <td>CREATE TABLE \"directors\" (\\n\"movie_id\" TEXT,\\n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>table</td>\n",
       "      <td>known_for</td>\n",
       "      <td>known_for</td>\n",
       "      <td>4</td>\n",
       "      <td>CREATE TABLE \"known_for\" (\\n\"person_id\" TEXT,\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>table</td>\n",
       "      <td>movie_akas</td>\n",
       "      <td>movie_akas</td>\n",
       "      <td>5</td>\n",
       "      <td>CREATE TABLE \"movie_akas\" (\\n\"movie_id\" TEXT,\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>table</td>\n",
       "      <td>movie_ratings</td>\n",
       "      <td>movie_ratings</td>\n",
       "      <td>6</td>\n",
       "      <td>CREATE TABLE \"movie_ratings\" (\\n\"movie_id\" TEX...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>table</td>\n",
       "      <td>persons</td>\n",
       "      <td>persons</td>\n",
       "      <td>7</td>\n",
       "      <td>CREATE TABLE \"persons\" (\\n\"person_id\" TEXT,\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>table</td>\n",
       "      <td>principals</td>\n",
       "      <td>principals</td>\n",
       "      <td>8</td>\n",
       "      <td>CREATE TABLE \"principals\" (\\n\"movie_id\" TEXT,\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>table</td>\n",
       "      <td>writers</td>\n",
       "      <td>writers</td>\n",
       "      <td>9</td>\n",
       "      <td>CREATE TABLE \"writers\" (\\n\"movie_id\" TEXT,\\n  ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    type           name       tbl_name  rootpage  \\\n",
       "0  table   movie_basics   movie_basics         2   \n",
       "1  table      directors      directors         3   \n",
       "2  table      known_for      known_for         4   \n",
       "3  table     movie_akas     movie_akas         5   \n",
       "4  table  movie_ratings  movie_ratings         6   \n",
       "5  table        persons        persons         7   \n",
       "6  table     principals     principals         8   \n",
       "7  table        writers        writers         9   \n",
       "\n",
       "                                                 sql  \n",
       "0  CREATE TABLE \"movie_basics\" (\\n\"movie_id\" TEXT...  \n",
       "1  CREATE TABLE \"directors\" (\\n\"movie_id\" TEXT,\\n...  \n",
       "2  CREATE TABLE \"known_for\" (\\n\"person_id\" TEXT,\\...  \n",
       "3  CREATE TABLE \"movie_akas\" (\\n\"movie_id\" TEXT,\\...  \n",
       "4  CREATE TABLE \"movie_ratings\" (\\n\"movie_id\" TEX...  \n",
       "5  CREATE TABLE \"persons\" (\\n\"person_id\" TEXT,\\n ...  \n",
       "6  CREATE TABLE \"principals\" (\\n\"movie_id\" TEXT,\\...  \n",
       "7  CREATE TABLE \"writers\" (\\n\"movie_id\" TEXT,\\n  ...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_schema = pd.read_sql(\"\"\"\n",
    "\n",
    "SELECT * from sqlite_master\n",
    "\n",
    "\"\"\",conn)\n",
    "imdb_schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>ordering</th>\n",
       "      <th>title</th>\n",
       "      <th>region</th>\n",
       "      <th>language</th>\n",
       "      <th>types</th>\n",
       "      <th>attributes</th>\n",
       "      <th>is_original_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>10</td>\n",
       "      <td>Джурасик свят</td>\n",
       "      <td>BG</td>\n",
       "      <td>bg</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>11</td>\n",
       "      <td>Jurashikku warudo</td>\n",
       "      <td>JP</td>\n",
       "      <td>None</td>\n",
       "      <td>imdbDisplay</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>12</td>\n",
       "      <td>Jurassic World: O Mundo dos Dinossauros</td>\n",
       "      <td>BR</td>\n",
       "      <td>None</td>\n",
       "      <td>imdbDisplay</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>13</td>\n",
       "      <td>O Mundo dos Dinossauros</td>\n",
       "      <td>BR</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>short title</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>14</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>FR</td>\n",
       "      <td>None</td>\n",
       "      <td>imdbDisplay</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id  ordering                                    title region  \\\n",
       "0  tt0369610        10                            Джурасик свят     BG   \n",
       "1  tt0369610        11                        Jurashikku warudo     JP   \n",
       "2  tt0369610        12  Jurassic World: O Mundo dos Dinossauros     BR   \n",
       "3  tt0369610        13                  O Mundo dos Dinossauros     BR   \n",
       "4  tt0369610        14                           Jurassic World     FR   \n",
       "\n",
       "  language        types   attributes  is_original_title  \n",
       "0       bg         None         None                0.0  \n",
       "1     None  imdbDisplay         None                0.0  \n",
       "2     None  imdbDisplay         None                0.0  \n",
       "3     None         None  short title                0.0  \n",
       "4     None  imdbDisplay         None                0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_akas_df = read_sql_as_df('movie_akas',conn)\n",
    "movie_akas_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt10356526</td>\n",
       "      <td>8.3</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt10384606</td>\n",
       "      <td>8.9</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt1042974</td>\n",
       "      <td>6.4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt1043726</td>\n",
       "      <td>4.2</td>\n",
       "      <td>50352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt1060240</td>\n",
       "      <td>6.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movie_id  averagerating  numvotes\n",
       "0  tt10356526            8.3        31\n",
       "1  tt10384606            8.9       559\n",
       "2   tt1042974            6.4        20\n",
       "3   tt1043726            4.2     50352\n",
       "4   tt1060240            6.5        21"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_ratings_df = read_sql_as_df('movie_ratings',conn)\n",
    "movie_ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>person_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0285252</td>\n",
       "      <td>nm0899854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0462036</td>\n",
       "      <td>nm1940585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0835418</td>\n",
       "      <td>nm0151540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0835418</td>\n",
       "      <td>nm0151540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0878654</td>\n",
       "      <td>nm0089502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id  person_id\n",
       "0  tt0285252  nm0899854\n",
       "1  tt0462036  nm1940585\n",
       "2  tt0835418  nm0151540\n",
       "3  tt0835418  nm0151540\n",
       "4  tt0878654  nm0089502"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors_df = read_sql_as_df('directors',conn)\n",
    "directors_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>person_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0285252</td>\n",
       "      <td>nm0899854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0438973</td>\n",
       "      <td>nm0175726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0438973</td>\n",
       "      <td>nm1802864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0462036</td>\n",
       "      <td>nm1940585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0835418</td>\n",
       "      <td>nm0310087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id  person_id\n",
       "0  tt0285252  nm0899854\n",
       "1  tt0438973  nm0175726\n",
       "2  tt0438973  nm1802864\n",
       "3  tt0462036  nm1940585\n",
       "4  tt0835418  nm0310087"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "writers_df = read_sql_as_df('writers',conn)\n",
    "writers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>original_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>146139</th>\n",
       "      <td>tt9916538</td>\n",
       "      <td>Kuambil Lagi Hatiku</td>\n",
       "      <td>Kuambil Lagi Hatiku</td>\n",
       "      <td>2019</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146140</th>\n",
       "      <td>tt9916622</td>\n",
       "      <td>Rodolpho Teóphilo - O Legado de um Pioneiro</td>\n",
       "      <td>Rodolpho Teóphilo - O Legado de um Pioneiro</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146141</th>\n",
       "      <td>tt9916706</td>\n",
       "      <td>Dankyavar Danka</td>\n",
       "      <td>Dankyavar Danka</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146142</th>\n",
       "      <td>tt9916730</td>\n",
       "      <td>6 Gunn</td>\n",
       "      <td>6 Gunn</td>\n",
       "      <td>2017</td>\n",
       "      <td>116.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146143</th>\n",
       "      <td>tt9916754</td>\n",
       "      <td>Chico Albuquerque - Revelações</td>\n",
       "      <td>Chico Albuquerque - Revelações</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id                                primary_title  \\\n",
       "146139  tt9916538                          Kuambil Lagi Hatiku   \n",
       "146140  tt9916622  Rodolpho Teóphilo - O Legado de um Pioneiro   \n",
       "146141  tt9916706                              Dankyavar Danka   \n",
       "146142  tt9916730                                       6 Gunn   \n",
       "146143  tt9916754               Chico Albuquerque - Revelações   \n",
       "\n",
       "                                     original_title  start_year  \\\n",
       "146139                          Kuambil Lagi Hatiku        2019   \n",
       "146140  Rodolpho Teóphilo - O Legado de um Pioneiro        2015   \n",
       "146141                              Dankyavar Danka        2013   \n",
       "146142                                       6 Gunn        2017   \n",
       "146143               Chico Albuquerque - Revelações        2013   \n",
       "\n",
       "        runtime_minutes       genres  \n",
       "146139            123.0        Drama  \n",
       "146140              NaN  Documentary  \n",
       "146141              NaN       Comedy  \n",
       "146142            116.0         None  \n",
       "146143              NaN  Documentary  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_basics_df = read_sql_as_df('movie_basics',conn)\n",
    "movie_basics_df.tail()\n",
    "# movie_basics.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_id</th>\n",
       "      <th>primary_name</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>death_year</th>\n",
       "      <th>primary_profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nm0061671</td>\n",
       "      <td>Mary Ellen Bauder</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miscellaneous,production_manager,producer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nm0061865</td>\n",
       "      <td>Joseph Bauer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>composer,music_department,sound_department</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nm0062070</td>\n",
       "      <td>Bruce Baum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miscellaneous,actor,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nm0062195</td>\n",
       "      <td>Axel Baumann</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>camera_department,cinematographer,art_department</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nm0062798</td>\n",
       "      <td>Pete Baxter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production_designer,art_department,set_decorator</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   person_id       primary_name  birth_year  death_year  \\\n",
       "0  nm0061671  Mary Ellen Bauder         NaN         NaN   \n",
       "1  nm0061865       Joseph Bauer         NaN         NaN   \n",
       "2  nm0062070         Bruce Baum         NaN         NaN   \n",
       "3  nm0062195       Axel Baumann         NaN         NaN   \n",
       "4  nm0062798        Pete Baxter         NaN         NaN   \n",
       "\n",
       "                                 primary_profession  \n",
       "0         miscellaneous,production_manager,producer  \n",
       "1        composer,music_department,sound_department  \n",
       "2                        miscellaneous,actor,writer  \n",
       "3  camera_department,cinematographer,art_department  \n",
       "4  production_designer,art_department,set_decorator  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "persons_df = read_sql_as_df('persons',conn)\n",
    "persons_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_id</th>\n",
       "      <th>movie_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nm0061671</td>\n",
       "      <td>tt0837562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nm0061671</td>\n",
       "      <td>tt2398241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nm0061671</td>\n",
       "      <td>tt0844471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nm0061671</td>\n",
       "      <td>tt0118553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nm0061865</td>\n",
       "      <td>tt0896534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   person_id   movie_id\n",
       "0  nm0061671  tt0837562\n",
       "1  nm0061671  tt2398241\n",
       "2  nm0061671  tt0844471\n",
       "3  nm0061671  tt0118553\n",
       "4  nm0061865  tt0896534"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "known_for_df = read_sql_as_df(\"known_for\",conn)\n",
    "known_for_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>ordering</th>\n",
       "      <th>person_id</th>\n",
       "      <th>category</th>\n",
       "      <th>job</th>\n",
       "      <th>characters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0111414</td>\n",
       "      <td>1</td>\n",
       "      <td>nm0246005</td>\n",
       "      <td>actor</td>\n",
       "      <td>None</td>\n",
       "      <td>[\"The Man\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0111414</td>\n",
       "      <td>2</td>\n",
       "      <td>nm0398271</td>\n",
       "      <td>director</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0111414</td>\n",
       "      <td>3</td>\n",
       "      <td>nm3739909</td>\n",
       "      <td>producer</td>\n",
       "      <td>producer</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0323808</td>\n",
       "      <td>10</td>\n",
       "      <td>nm0059247</td>\n",
       "      <td>editor</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0323808</td>\n",
       "      <td>1</td>\n",
       "      <td>nm3579312</td>\n",
       "      <td>actress</td>\n",
       "      <td>None</td>\n",
       "      <td>[\"Beth Boothby\"]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id  ordering  person_id  category       job        characters\n",
       "0  tt0111414         1  nm0246005     actor      None       [\"The Man\"]\n",
       "1  tt0111414         2  nm0398271  director      None              None\n",
       "2  tt0111414         3  nm3739909  producer  producer              None\n",
       "3  tt0323808        10  nm0059247    editor      None              None\n",
       "4  tt0323808         1  nm3579312   actress      None  [\"Beth Boothby\"]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principals_df = read_sql_as_df(\"principals\",conn)\n",
    "principals_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non database data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rotten Tomatoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rotten_tomatoes_link</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>movie_info</th>\n",
       "      <th>critics_consensus</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genres</th>\n",
       "      <th>directors</th>\n",
       "      <th>authors</th>\n",
       "      <th>actors</th>\n",
       "      <th>original_release_date</th>\n",
       "      <th>...</th>\n",
       "      <th>production_company</th>\n",
       "      <th>tomatometer_status</th>\n",
       "      <th>tomatometer_rating</th>\n",
       "      <th>tomatometer_count</th>\n",
       "      <th>audience_status</th>\n",
       "      <th>audience_rating</th>\n",
       "      <th>audience_count</th>\n",
       "      <th>tomatometer_top_critics_count</th>\n",
       "      <th>tomatometer_fresh_critics_count</th>\n",
       "      <th>tomatometer_rotten_critics_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>Always trouble-prone, the life of teenager Per...</td>\n",
       "      <td>Though it may seem like just another Harry Pot...</td>\n",
       "      <td>PG</td>\n",
       "      <td>Action &amp; Adventure, Comedy, Drama, Science Fic...</td>\n",
       "      <td>Chris Columbus</td>\n",
       "      <td>Craig Titley, Chris Columbus, Rick Riordan</td>\n",
       "      <td>Logan Lerman, Brandon T. Jackson, Alexandra Da...</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>...</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>Rotten</td>\n",
       "      <td>49.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>Spilled</td>\n",
       "      <td>53.0</td>\n",
       "      <td>254421.0</td>\n",
       "      <td>43</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m/0878835</td>\n",
       "      <td>Please Give</td>\n",
       "      <td>Kate (Catherine Keener) and her husband Alex (...</td>\n",
       "      <td>Nicole Holofcener's newest might seem slight i...</td>\n",
       "      <td>R</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Nicole Holofcener</td>\n",
       "      <td>Nicole Holofcener</td>\n",
       "      <td>Catherine Keener, Amanda Peet, Oliver Platt, R...</td>\n",
       "      <td>2010-04-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Sony Pictures Classics</td>\n",
       "      <td>Certified-Fresh</td>\n",
       "      <td>87.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>Upright</td>\n",
       "      <td>64.0</td>\n",
       "      <td>11574.0</td>\n",
       "      <td>44</td>\n",
       "      <td>123</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>m/10</td>\n",
       "      <td>10</td>\n",
       "      <td>A successful, middle-aged Hollywood songwriter...</td>\n",
       "      <td>Blake Edwards' bawdy comedy may not score a pe...</td>\n",
       "      <td>R</td>\n",
       "      <td>Comedy, Romance</td>\n",
       "      <td>Blake Edwards</td>\n",
       "      <td>Blake Edwards</td>\n",
       "      <td>Dudley Moore, Bo Derek, Julie Andrews, Robert ...</td>\n",
       "      <td>1979-10-05</td>\n",
       "      <td>...</td>\n",
       "      <td>Waner Bros.</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>67.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Spilled</td>\n",
       "      <td>53.0</td>\n",
       "      <td>14684.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m/1000013-12_angry_men</td>\n",
       "      <td>12 Angry Men (Twelve Angry Men)</td>\n",
       "      <td>Following the closing arguments in a murder tr...</td>\n",
       "      <td>Sidney Lumet's feature debut is a superbly wri...</td>\n",
       "      <td>NR</td>\n",
       "      <td>Classics, Drama</td>\n",
       "      <td>Sidney Lumet</td>\n",
       "      <td>Reginald Rose</td>\n",
       "      <td>Martin Balsam, John Fiedler, Lee J. Cobb, E.G....</td>\n",
       "      <td>1957-04-13</td>\n",
       "      <td>...</td>\n",
       "      <td>Criterion Collection</td>\n",
       "      <td>Certified-Fresh</td>\n",
       "      <td>100.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Upright</td>\n",
       "      <td>97.0</td>\n",
       "      <td>105386.0</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>m/1000079-20000_leagues_under_the_sea</td>\n",
       "      <td>20,000 Leagues Under The Sea</td>\n",
       "      <td>In 1866, Professor Pierre M. Aronnax (Paul Luk...</td>\n",
       "      <td>One of Disney's finest live-action adventures,...</td>\n",
       "      <td>G</td>\n",
       "      <td>Action &amp; Adventure, Drama, Kids &amp; Family</td>\n",
       "      <td>Richard Fleischer</td>\n",
       "      <td>Earl Felton</td>\n",
       "      <td>James Mason, Kirk Douglas, Paul Lukas, Peter L...</td>\n",
       "      <td>1954-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Disney</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>89.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Upright</td>\n",
       "      <td>74.0</td>\n",
       "      <td>68918.0</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    rotten_tomatoes_link  \\\n",
       "0                              m/0814255   \n",
       "1                              m/0878835   \n",
       "2                                   m/10   \n",
       "3                 m/1000013-12_angry_men   \n",
       "4  m/1000079-20000_leagues_under_the_sea   \n",
       "\n",
       "                                         movie_title  \\\n",
       "0  Percy Jackson & the Olympians: The Lightning T...   \n",
       "1                                        Please Give   \n",
       "2                                                 10   \n",
       "3                    12 Angry Men (Twelve Angry Men)   \n",
       "4                       20,000 Leagues Under The Sea   \n",
       "\n",
       "                                          movie_info  \\\n",
       "0  Always trouble-prone, the life of teenager Per...   \n",
       "1  Kate (Catherine Keener) and her husband Alex (...   \n",
       "2  A successful, middle-aged Hollywood songwriter...   \n",
       "3  Following the closing arguments in a murder tr...   \n",
       "4  In 1866, Professor Pierre M. Aronnax (Paul Luk...   \n",
       "\n",
       "                                   critics_consensus content_rating  \\\n",
       "0  Though it may seem like just another Harry Pot...             PG   \n",
       "1  Nicole Holofcener's newest might seem slight i...              R   \n",
       "2  Blake Edwards' bawdy comedy may not score a pe...              R   \n",
       "3  Sidney Lumet's feature debut is a superbly wri...             NR   \n",
       "4  One of Disney's finest live-action adventures,...              G   \n",
       "\n",
       "                                              genres          directors  \\\n",
       "0  Action & Adventure, Comedy, Drama, Science Fic...     Chris Columbus   \n",
       "1                                             Comedy  Nicole Holofcener   \n",
       "2                                    Comedy, Romance      Blake Edwards   \n",
       "3                                    Classics, Drama       Sidney Lumet   \n",
       "4           Action & Adventure, Drama, Kids & Family  Richard Fleischer   \n",
       "\n",
       "                                      authors  \\\n",
       "0  Craig Titley, Chris Columbus, Rick Riordan   \n",
       "1                           Nicole Holofcener   \n",
       "2                               Blake Edwards   \n",
       "3                               Reginald Rose   \n",
       "4                                 Earl Felton   \n",
       "\n",
       "                                              actors original_release_date  \\\n",
       "0  Logan Lerman, Brandon T. Jackson, Alexandra Da...            2010-02-12   \n",
       "1  Catherine Keener, Amanda Peet, Oliver Platt, R...            2010-04-30   \n",
       "2  Dudley Moore, Bo Derek, Julie Andrews, Robert ...            1979-10-05   \n",
       "3  Martin Balsam, John Fiedler, Lee J. Cobb, E.G....            1957-04-13   \n",
       "4  James Mason, Kirk Douglas, Paul Lukas, Peter L...            1954-01-01   \n",
       "\n",
       "   ...      production_company  tomatometer_status tomatometer_rating  \\\n",
       "0  ...        20th Century Fox              Rotten               49.0   \n",
       "1  ...  Sony Pictures Classics     Certified-Fresh               87.0   \n",
       "2  ...             Waner Bros.               Fresh               67.0   \n",
       "3  ...    Criterion Collection     Certified-Fresh              100.0   \n",
       "4  ...                  Disney               Fresh               89.0   \n",
       "\n",
       "  tomatometer_count  audience_status  audience_rating audience_count  \\\n",
       "0             149.0          Spilled             53.0       254421.0   \n",
       "1             142.0          Upright             64.0        11574.0   \n",
       "2              24.0          Spilled             53.0        14684.0   \n",
       "3              54.0          Upright             97.0       105386.0   \n",
       "4              27.0          Upright             74.0        68918.0   \n",
       "\n",
       "   tomatometer_top_critics_count  tomatometer_fresh_critics_count  \\\n",
       "0                             43                               73   \n",
       "1                             44                              123   \n",
       "2                              2                               16   \n",
       "3                              6                               54   \n",
       "4                              5                               24   \n",
       "\n",
       "   tomatometer_rotten_critics_count  \n",
       "0                                76  \n",
       "1                                19  \n",
       "2                                 8  \n",
       "3                                 0  \n",
       "4                                 3  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt_movies_df = pd.read_csv('./zippedData/rotten_tomatoes_movies.csv')\n",
    "rt_movies_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The number (budgets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5782 entries, 0 to 5781\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   id                 5782 non-null   int64 \n",
      " 1   release_date       5782 non-null   object\n",
      " 2   movie              5782 non-null   object\n",
      " 3   production_budget  5782 non-null   object\n",
      " 4   domestic_gross     5782 non-null   object\n",
      " 5   worldwide_gross    5782 non-null   object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 271.2+ KB\n"
     ]
    }
   ],
   "source": [
    "movie_budget_df = pd.read_csv('./zippedData/tn.movie_budgets.csv.gz')\n",
    "movie_budget_df.head()\n",
    "movie_budget_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Box office mojo (gross profit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3387 entries, 0 to 3386\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   title           3387 non-null   object \n",
      " 1   studio          3382 non-null   object \n",
      " 2   domestic_gross  3359 non-null   float64\n",
      " 3   foreign_gross   2037 non-null   object \n",
      " 4   year            3387 non-null   int64  \n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 132.4+ KB\n"
     ]
    }
   ],
   "source": [
    "movie_gross_df = pd.read_csv('./zippedData/bom.movie_gross.csv.gz')\n",
    "movie_gross_df.head()\n",
    "movie_gross_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TMDB movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-f1f9ac6d6bb5>:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  ds[idx] = row.replace(char,\"\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>genre_ids</th>\n",
       "      <th>id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12, 14, 10751</td>\n",
       "      <td>12444</td>\n",
       "      <td>en</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>33.533</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>14, 12, 16, 10751</td>\n",
       "      <td>10191</td>\n",
       "      <td>en</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>28.734</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>12, 28, 878</td>\n",
       "      <td>10138</td>\n",
       "      <td>en</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>28.515</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>12368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>16, 35, 10751</td>\n",
       "      <td>862</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>28.005</td>\n",
       "      <td>1995-11-22</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>28, 878, 12</td>\n",
       "      <td>27205</td>\n",
       "      <td>en</td>\n",
       "      <td>Inception</td>\n",
       "      <td>27.920</td>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>Inception</td>\n",
       "      <td>8.3</td>\n",
       "      <td>22186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          genre_ids     id original_language  \\\n",
       "0           0      12, 14, 10751  12444                en   \n",
       "1           1  14, 12, 16, 10751  10191                en   \n",
       "2           2        12, 28, 878  10138                en   \n",
       "3           3      16, 35, 10751    862                en   \n",
       "4           4        28, 878, 12  27205                en   \n",
       "\n",
       "                                 original_title  popularity release_date  \\\n",
       "0  Harry Potter and the Deathly Hallows: Part 1      33.533   2010-11-19   \n",
       "1                      How to Train Your Dragon      28.734   2010-03-26   \n",
       "2                                    Iron Man 2      28.515   2010-05-07   \n",
       "3                                     Toy Story      28.005   1995-11-22   \n",
       "4                                     Inception      27.920   2010-07-16   \n",
       "\n",
       "                                          title  vote_average  vote_count  \n",
       "0  Harry Potter and the Deathly Hallows: Part 1           7.7       10788  \n",
       "1                      How to Train Your Dragon           7.7        7610  \n",
       "2                                    Iron Man 2           6.8       12368  \n",
       "3                                     Toy Story           7.9       10174  \n",
       "4                                     Inception           8.3       22186  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmdb_movies_df = pd.read_csv('./zippedData/tmdb.movies.csv.gz')\n",
    "remove_chars(tmdb_movies_df['genre_ids'],[\"[\",\"]\"]) #genre_ids now is a string. Function turns it into a list.\n",
    "\n",
    "tmdb_movies_df.head()\n",
    "# tmdb_movies_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rreplace field that's entirely space (or empty) with '101', which is the genre key for genre \"Unknown\"\n",
    "tmdb_movies_df['genre_ids'] = tmdb_movies_df['genre_ids'].str.replace(r'^\\s*$','101')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>genre_names</th>\n",
       "      <th>Action</th>\n",
       "      <th>...</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Music</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Science Fiction</th>\n",
       "      <th>TV Movie</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15458</th>\n",
       "      <td>15458</td>\n",
       "      <td>379050</td>\n",
       "      <td>At Granny's House</td>\n",
       "      <td>1.564</td>\n",
       "      <td>2015-05-30</td>\n",
       "      <td>At Granny's House</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5</td>\n",
       "      <td>[Thriller]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4089</th>\n",
       "      <td>4089</td>\n",
       "      <td>127747</td>\n",
       "      <td>Open Gate</td>\n",
       "      <td>0.868</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>Open Gate</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5</td>\n",
       "      <td>[Drama]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>14987</td>\n",
       "      <td>369057</td>\n",
       "      <td>A Prince for Christmas</td>\n",
       "      <td>3.088</td>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>A Prince for Christmas</td>\n",
       "      <td>5.7</td>\n",
       "      <td>35</td>\n",
       "      <td>[Romance, TV Movie]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6832</th>\n",
       "      <td>6832</td>\n",
       "      <td>170884</td>\n",
       "      <td>Stemple Pass</td>\n",
       "      <td>0.716</td>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>Stemple Pass</td>\n",
       "      <td>8.8</td>\n",
       "      <td>4</td>\n",
       "      <td>[Documentary]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14040</th>\n",
       "      <td>14040</td>\n",
       "      <td>302584</td>\n",
       "      <td>Thr33 Days Dead</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2014-11-11</td>\n",
       "      <td>Thr33 Days Dead</td>\n",
       "      <td>3.7</td>\n",
       "      <td>5</td>\n",
       "      <td>[Action, Horror]</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>1676</td>\n",
       "      <td>61162</td>\n",
       "      <td>Obituary: Live Xecution</td>\n",
       "      <td>0.654</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Obituary: Live Xecution</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2</td>\n",
       "      <td>[Music]</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>18806</th>\n",
       "      <td>18806</td>\n",
       "      <td>447664</td>\n",
       "      <td>Dead Draw</td>\n",
       "      <td>1.454</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>Dead Draw</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19</td>\n",
       "      <td>[Thriller, Crime]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7825</th>\n",
       "      <td>7825</td>\n",
       "      <td>260622</td>\n",
       "      <td>Dystopia: 2013</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2012-01-15</td>\n",
       "      <td>Dystopia: 2013</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[Science Fiction]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7705</th>\n",
       "      <td>7705</td>\n",
       "      <td>289442</td>\n",
       "      <td>Closed Circuit Extreme</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2012-09-24</td>\n",
       "      <td>Closed Circuit Extreme</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4</td>\n",
       "      <td>[Horror]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4159</th>\n",
       "      <td>4159</td>\n",
       "      <td>274343</td>\n",
       "      <td>The Ghost of Saint Aubin</td>\n",
       "      <td>0.840</td>\n",
       "      <td>2011-09-01</td>\n",
       "      <td>The Ghost of Saint Aubin</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[Science Fiction]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4431</th>\n",
       "      <td>4431</td>\n",
       "      <td>602984</td>\n",
       "      <td>Lucky</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2011-03-11</td>\n",
       "      <td>Lucky</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[Drama, Romance]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12629</th>\n",
       "      <td>12629</td>\n",
       "      <td>250853</td>\n",
       "      <td>Her: Love in the Modern Age</td>\n",
       "      <td>1.132</td>\n",
       "      <td>2014-01-17</td>\n",
       "      <td>Her: Love in the Modern Age</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[Documentary]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19440</th>\n",
       "      <td>19440</td>\n",
       "      <td>428010</td>\n",
       "      <td>Liberty's Secret</td>\n",
       "      <td>0.787</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>Liberty's Secret</td>\n",
       "      <td>4.7</td>\n",
       "      <td>6</td>\n",
       "      <td>[Comedy, Romance, Music]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8025</th>\n",
       "      <td>8025</td>\n",
       "      <td>196254</td>\n",
       "      <td>Barbie: Mariposa and The Fairy Princess</td>\n",
       "      <td>9.772</td>\n",
       "      <td>2013-08-27</td>\n",
       "      <td>Barbie: Mariposa and The Fairy Princess</td>\n",
       "      <td>6.5</td>\n",
       "      <td>133</td>\n",
       "      <td>[Animation, Family, Fantasy]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21708</th>\n",
       "      <td>21708</td>\n",
       "      <td>428886</td>\n",
       "      <td>England Is Mine</td>\n",
       "      <td>2.454</td>\n",
       "      <td>2017-08-25</td>\n",
       "      <td>England Is Mine</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32</td>\n",
       "      <td>[Drama]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3046</th>\n",
       "      <td>3046</td>\n",
       "      <td>60089</td>\n",
       "      <td>Best Player</td>\n",
       "      <td>4.389</td>\n",
       "      <td>2011-03-12</td>\n",
       "      <td>Best Player</td>\n",
       "      <td>5.7</td>\n",
       "      <td>62</td>\n",
       "      <td>[Comedy, Family]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3757</th>\n",
       "      <td>3757</td>\n",
       "      <td>64645</td>\n",
       "      <td>Svadba po Obmenu</td>\n",
       "      <td>1.400</td>\n",
       "      <td>2011-11-11</td>\n",
       "      <td>Exchange Wedding</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>[Comedy, Romance, Drama]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12551</th>\n",
       "      <td>12551</td>\n",
       "      <td>300685</td>\n",
       "      <td>Jeff Dunham: All Over the Map</td>\n",
       "      <td>1.270</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>Jeff Dunham: All Over the Map</td>\n",
       "      <td>5.3</td>\n",
       "      <td>11</td>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22370</th>\n",
       "      <td>22370</td>\n",
       "      <td>473420</td>\n",
       "      <td>Bob Dylan: Trouble No More</td>\n",
       "      <td>1.178</td>\n",
       "      <td>2017-10-02</td>\n",
       "      <td>Bob Dylan: Trouble No More</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>[Music, Documentary]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9409</th>\n",
       "      <td>9409</td>\n",
       "      <td>314387</td>\n",
       "      <td>Scooby-Doo! Mecha Mutt Menace</td>\n",
       "      <td>1.145</td>\n",
       "      <td>2013-09-24</td>\n",
       "      <td>Scooby-Doo! Mecha Mutt Menace</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3</td>\n",
       "      <td>[Family, Comedy, Mystery, Animation]</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0      id                           original_title  \\\n",
       "15458       15458  379050                        At Granny's House   \n",
       "4089         4089  127747                                Open Gate   \n",
       "14987       14987  369057                   A Prince for Christmas   \n",
       "6832         6832  170884                             Stemple Pass   \n",
       "14040       14040  302584                          Thr33 Days Dead   \n",
       "1676         1676   61162                  Obituary: Live Xecution   \n",
       "18806       18806  447664                                Dead Draw   \n",
       "7825         7825  260622                           Dystopia: 2013   \n",
       "7705         7705  289442                   Closed Circuit Extreme   \n",
       "4159         4159  274343                 The Ghost of Saint Aubin   \n",
       "4431         4431  602984                                    Lucky   \n",
       "12629       12629  250853              Her: Love in the Modern Age   \n",
       "19440       19440  428010                         Liberty's Secret   \n",
       "8025         8025  196254  Barbie: Mariposa and The Fairy Princess   \n",
       "21708       21708  428886                          England Is Mine   \n",
       "3046         3046   60089                              Best Player   \n",
       "3757         3757   64645                         Svadba po Obmenu   \n",
       "12551       12551  300685            Jeff Dunham: All Over the Map   \n",
       "22370       22370  473420               Bob Dylan: Trouble No More   \n",
       "9409         9409  314387            Scooby-Doo! Mecha Mutt Menace   \n",
       "\n",
       "       popularity release_date                                    title  \\\n",
       "15458       1.564   2015-05-30                        At Granny's House   \n",
       "4089        0.868   2011-01-01                                Open Gate   \n",
       "14987       3.088   2015-11-29                   A Prince for Christmas   \n",
       "6832        0.716   2012-10-01                             Stemple Pass   \n",
       "14040       0.600   2014-11-11                          Thr33 Days Dead   \n",
       "1676        0.654   2010-01-01                  Obituary: Live Xecution   \n",
       "18806       1.454   2016-09-30                                Dead Draw   \n",
       "7825        0.600   2012-01-15                           Dystopia: 2013   \n",
       "7705        0.600   2012-09-24                   Closed Circuit Extreme   \n",
       "4159        0.840   2011-09-01                 The Ghost of Saint Aubin   \n",
       "4431        0.600   2011-03-11                                    Lucky   \n",
       "12629       1.132   2014-01-17              Her: Love in the Modern Age   \n",
       "19440       0.787   2016-11-04                         Liberty's Secret   \n",
       "8025        9.772   2013-08-27  Barbie: Mariposa and The Fairy Princess   \n",
       "21708       2.454   2017-08-25                          England Is Mine   \n",
       "3046        4.389   2011-03-12                              Best Player   \n",
       "3757        1.400   2011-11-11                         Exchange Wedding   \n",
       "12551       1.270   2014-11-18            Jeff Dunham: All Over the Map   \n",
       "22370       1.178   2017-10-02               Bob Dylan: Trouble No More   \n",
       "9409        1.145   2013-09-24            Scooby-Doo! Mecha Mutt Menace   \n",
       "\n",
       "       vote_average  vote_count                           genre_names  Action  \\\n",
       "15458           3.3           5                            [Thriller]   False   \n",
       "4089            5.9           5                               [Drama]   False   \n",
       "14987           5.7          35                   [Romance, TV Movie]   False   \n",
       "6832            8.8           4                         [Documentary]   False   \n",
       "14040           3.7           5                      [Action, Horror]    True   \n",
       "1676            4.3           2                               [Music]   False   \n",
       "18806           5.0          19                     [Thriller, Crime]   False   \n",
       "7825            2.0           1                     [Science Fiction]   False   \n",
       "7705            4.4           4                              [Horror]   False   \n",
       "4159            1.0           1                     [Science Fiction]   False   \n",
       "4431           10.0           1                      [Drama, Romance]   False   \n",
       "12629           6.0           1                         [Documentary]   False   \n",
       "19440           4.7           6              [Comedy, Romance, Music]   False   \n",
       "8025            6.5         133          [Animation, Family, Fantasy]   False   \n",
       "21708           6.0          32                               [Drama]   False   \n",
       "3046            5.7          62                      [Comedy, Family]   False   \n",
       "3757            3.5           2              [Comedy, Romance, Drama]   False   \n",
       "12551           5.3          11                              [Comedy]   False   \n",
       "22370           6.0           2                  [Music, Documentary]   False   \n",
       "9409            7.3           3  [Family, Comedy, Mystery, Animation]   False   \n",
       "\n",
       "       ...  Horror  Music  Mystery  Romance  Science Fiction  TV Movie  \\\n",
       "15458  ...   False  False    False    False            False     False   \n",
       "4089   ...   False  False    False    False            False     False   \n",
       "14987  ...   False  False    False     True            False      True   \n",
       "6832   ...   False  False    False    False            False     False   \n",
       "14040  ...    True  False    False    False            False     False   \n",
       "1676   ...   False   True    False    False            False     False   \n",
       "18806  ...   False  False    False    False            False     False   \n",
       "7825   ...   False  False    False    False             True     False   \n",
       "7705   ...    True  False    False    False            False     False   \n",
       "4159   ...   False  False    False    False             True     False   \n",
       "4431   ...   False  False    False     True            False     False   \n",
       "12629  ...   False  False    False    False            False     False   \n",
       "19440  ...   False   True    False     True            False     False   \n",
       "8025   ...   False  False    False    False            False     False   \n",
       "21708  ...   False  False    False    False            False     False   \n",
       "3046   ...   False  False    False    False            False     False   \n",
       "3757   ...   False  False    False     True            False     False   \n",
       "12551  ...   False  False    False    False            False     False   \n",
       "22370  ...   False   True    False    False            False     False   \n",
       "9409   ...   False  False     True    False            False     False   \n",
       "\n",
       "       Thriller    War  Western  Unknown  \n",
       "15458      True  False    False    False  \n",
       "4089      False  False    False    False  \n",
       "14987     False  False    False    False  \n",
       "6832      False  False    False    False  \n",
       "14040     False  False    False    False  \n",
       "1676      False  False    False    False  \n",
       "18806      True  False    False    False  \n",
       "7825      False  False    False    False  \n",
       "7705      False  False    False    False  \n",
       "4159      False  False    False    False  \n",
       "4431      False  False    False    False  \n",
       "12629     False  False    False    False  \n",
       "19440     False  False    False    False  \n",
       "8025      False  False    False    False  \n",
       "21708     False  False    False    False  \n",
       "3046      False  False    False    False  \n",
       "3757      False  False    False    False  \n",
       "12551     False  False    False    False  \n",
       "22370     False  False    False    False  \n",
       "9409      False  False    False    False  \n",
       "\n",
       "[20 rows x 29 columns]"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmdb_movies_df.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get genre ID from TMDB using API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from keys import tmdb_key #keys is the python file \n",
    "\n",
    "tmdb_2 = tmdb_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# tmdb_url = f\"https://api.themoviedb.org/3/genre/movie/list?api_key={tmdb_2}&language=en-US\"\n",
    "\n",
    "# #get response\n",
    "# resp_tmdb = requests.get(tmdb_url)\n",
    "\n",
    "# tmdb_json = resp_tmdb.json()\n",
    "# tmdb_json['genres']\n",
    "# tmdb_genre= {num['id']:num['name'] for num in tmdb_json['genres']}\n",
    "# tmdb_genre[101] = 'Unknown'\n",
    "# tmdb_genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmdb_genre= {28: 'Action',\n",
    " 12: 'Adventure',\n",
    " 16: 'Animation',\n",
    " 35: 'Comedy',\n",
    " 80: 'Crime',\n",
    " 99: 'Documentary',\n",
    " 18: 'Drama',\n",
    " 10751: 'Family',\n",
    " 14: 'Fantasy',\n",
    " 36: 'History',\n",
    " 27: 'Horror',\n",
    " 10402: 'Music',\n",
    " 9648: 'Mystery',\n",
    " 10749: 'Romance',\n",
    " 878: 'Science Fiction',\n",
    " 10770: 'TV Movie',\n",
    " 53: 'Thriller',\n",
    " 10752: 'War',\n",
    " 37: 'Western',\n",
    " 101: 'Unknown'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for genre_list in tmdb_movies_df['genre_ids']:\n",
    "#     genre_list = genre_list.split(', ')\n",
    "#     genre_names=[]\n",
    "#     for genre_id in genre_list:\n",
    "#         if genre_id\n",
    "#         genre_id=int(genre_id)\n",
    "# #         print(type(genre_id))\n",
    "#         genre_names = [tmdb_genre[genre_id]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                                  517\n",
       "genre_ids                                   101\n",
       "id                                        31059\n",
       "original_language                            ru\n",
       "original_title         Наша Russia: Яйца судьбы\n",
       "popularity                                3.867\n",
       "release_date                         2010-01-21\n",
       "title                Nasha Russia: Yaytsa sudby\n",
       "vote_average                                4.3\n",
       "vote_count                                   25\n",
       "Name: 517, dtype: object"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmdb_movies_df.iloc[517]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_names_lists=[]\n",
    "idx = 0\n",
    "for genre_list in tmdb_movies_df['genre_ids']:\n",
    "    genre_list = genre_list.split(', ')\n",
    "    genre_names=[]\n",
    "    genre_names = [tmdb_genre[int(genre_id)] for genre_id in genre_list]\n",
    "    genre_names_lists.append(genre_names)\n",
    "tmdb_movies_df['genre_names'] = genre_names_lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26517 entries, 0 to 26516\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         26517 non-null  int64  \n",
      " 1   genre_ids          26517 non-null  object \n",
      " 2   id                 26517 non-null  int64  \n",
      " 3   original_language  26517 non-null  object \n",
      " 4   original_title     26517 non-null  object \n",
      " 5   popularity         26517 non-null  float64\n",
      " 6   release_date       26517 non-null  object \n",
      " 7   title              26517 non-null  object \n",
      " 8   vote_average       26517 non-null  float64\n",
      " 9   vote_count         26517 non-null  int64  \n",
      " 10  genre_names        26517 non-null  object \n",
      "dtypes: float64(2), int64(3), object(6)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "tmdb_movies_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmdb_movies_df = tmdb_movies_df.drop(['original_language','genre_ids'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>genre_names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12444</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>33.533</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10788</td>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10191</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>28.734</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7610</td>\n",
       "      <td>[Fantasy, Adventure, Animation, Family]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10138</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>28.515</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>12368</td>\n",
       "      <td>[Adventure, Action, Science Fiction]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>862</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>28.005</td>\n",
       "      <td>1995-11-22</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10174</td>\n",
       "      <td>[Animation, Comedy, Family]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>27205</td>\n",
       "      <td>Inception</td>\n",
       "      <td>27.920</td>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>Inception</td>\n",
       "      <td>8.3</td>\n",
       "      <td>22186</td>\n",
       "      <td>[Action, Science Fiction, Adventure]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     id                                original_title  \\\n",
       "0           0  12444  Harry Potter and the Deathly Hallows: Part 1   \n",
       "1           1  10191                      How to Train Your Dragon   \n",
       "2           2  10138                                    Iron Man 2   \n",
       "3           3    862                                     Toy Story   \n",
       "4           4  27205                                     Inception   \n",
       "\n",
       "   popularity release_date                                         title  \\\n",
       "0      33.533   2010-11-19  Harry Potter and the Deathly Hallows: Part 1   \n",
       "1      28.734   2010-03-26                      How to Train Your Dragon   \n",
       "2      28.515   2010-05-07                                    Iron Man 2   \n",
       "3      28.005   1995-11-22                                     Toy Story   \n",
       "4      27.920   2010-07-16                                     Inception   \n",
       "\n",
       "   vote_average  vote_count                              genre_names  \n",
       "0           7.7       10788             [Adventure, Fantasy, Family]  \n",
       "1           7.7        7610  [Fantasy, Adventure, Animation, Family]  \n",
       "2           6.8       12368     [Adventure, Action, Science Fiction]  \n",
       "3           7.9       10174              [Animation, Comedy, Family]  \n",
       "4           8.3       22186     [Action, Science Fiction, Adventure]  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmdb_movies_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start of preliminary Data exploratory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Genre popularity using IM.DB data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 73856 entries, 0 to 73855\n",
      "Data columns (total 7 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   movie_id         73856 non-null  object \n",
      " 1   primary_title    73856 non-null  object \n",
      " 2   start_year       73856 non-null  int64  \n",
      " 3   runtime_minutes  66236 non-null  float64\n",
      " 4   genres           73052 non-null  object \n",
      " 5   averagerating    73856 non-null  float64\n",
      " 6   numvotes         73856 non-null  int64  \n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 3.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#Join the table movie_basics and movie_ratings to start exploring popularity and average reviews by genre.\n",
    "\n",
    "\n",
    "movie_basics_reviews = pd.read_sql(\"\"\"\n",
    "SELECT mb.movie_id, mb.primary_title ,mb.start_year,mb.runtime_minutes, mb.genres\n",
    "        ,r.averagerating, r.numvotes\n",
    "        \n",
    "FROM movie_basics as mb\n",
    "INNER JOIN movie_ratings as r\n",
    "        ON mb.movie_id = r.movie_id\n",
    "\n",
    "\"\"\",conn)\n",
    "movie_basics_reviews.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id              0\n",
       "primary_title         0\n",
       "start_year            0\n",
       "runtime_minutes    7620\n",
       "genres              804\n",
       "averagerating         0\n",
       "numvotes              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_basics_reviews.isna().sum() #checkout null values in our new table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop rows with null values in the genre column\n",
    "movie_basics_reviews_cleaned = movie_basics_reviews.dropna(subset =['genres']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>7.0</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>114.0</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>7.2</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>6.1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>2017</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>6.5</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73047</th>\n",
       "      <td>tt9913056</td>\n",
       "      <td>Swarm Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73048</th>\n",
       "      <td>tt9913084</td>\n",
       "      <td>Diabolik sono io</td>\n",
       "      <td>2019</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73049</th>\n",
       "      <td>tt9914286</td>\n",
       "      <td>Sokagin Çocuklari</td>\n",
       "      <td>2019</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Drama,Family</td>\n",
       "      <td>8.7</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73050</th>\n",
       "      <td>tt9914642</td>\n",
       "      <td>Albatross</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73051</th>\n",
       "      <td>tt9916160</td>\n",
       "      <td>Drømmeland</td>\n",
       "      <td>2019</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73052 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        movie_id                    primary_title  start_year  \\\n",
       "0      tt0063540                        Sunghursh        2013   \n",
       "1      tt0066787  One Day Before the Rainy Season        2019   \n",
       "2      tt0069049       The Other Side of the Wind        2018   \n",
       "3      tt0069204                  Sabse Bada Sukh        2018   \n",
       "4      tt0100275         The Wandering Soap Opera        2017   \n",
       "...          ...                              ...         ...   \n",
       "73047  tt9913056                     Swarm Season        2019   \n",
       "73048  tt9913084                 Diabolik sono io        2019   \n",
       "73049  tt9914286                Sokagin Çocuklari        2019   \n",
       "73050  tt9914642                        Albatross        2017   \n",
       "73051  tt9916160                       Drømmeland        2019   \n",
       "\n",
       "       runtime_minutes                genres  averagerating  numvotes  \n",
       "0                175.0    Action,Crime,Drama            7.0        77  \n",
       "1                114.0       Biography,Drama            7.2        43  \n",
       "2                122.0                 Drama            6.9      4517  \n",
       "3                  NaN          Comedy,Drama            6.1        13  \n",
       "4                 80.0  Comedy,Drama,Fantasy            6.5       119  \n",
       "...                ...                   ...            ...       ...  \n",
       "73047             86.0           Documentary            6.2         5  \n",
       "73048             75.0           Documentary            6.2         6  \n",
       "73049             98.0          Drama,Family            8.7       136  \n",
       "73050              NaN           Documentary            8.5         8  \n",
       "73051             72.0           Documentary            6.5        11  \n",
       "\n",
       "[73052 rows x 7 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reset the table index after dropping the null rows\n",
    "movie_basics_reviews_cleaned=movie_basics_reviews_cleaned.reset_index(drop=True)\n",
    "\n",
    "movie_basics_reviews_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#List of common film genres\n",
    "\n",
    "genre_list=[\"Action\",\"Comedy\",\"Drama\",\"Fantasy\",\"Horror\",\"Mystery\",\"Romance\",\"Thriller\",\"Western\",\"Sci-Fi\",\"Animation\"\n",
    "            ,\"Documentary\",\"Biography\",\"Crime\",\"Musical\",\"History\",\"Family\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make a new column for each genre with boolean values. True if genre is one of the movie's genre list.\n",
    "\n",
    "for genre in genre_list:\n",
    "    movie_basics_reviews_cleaned[genre]=[True if genre in movie else False for movie in movie_basics_reviews_cleaned['genres']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "      <th>Action</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Drama</th>\n",
       "      <th>...</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>Western</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Biography</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Musical</th>\n",
       "      <th>History</th>\n",
       "      <th>Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>7.0</td>\n",
       "      <td>77</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>114.0</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>7.2</td>\n",
       "      <td>43</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4517</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>6.1</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>2017</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>6.5</td>\n",
       "      <td>119</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id                    primary_title  start_year  runtime_minutes  \\\n",
       "0  tt0063540                        Sunghursh        2013            175.0   \n",
       "1  tt0066787  One Day Before the Rainy Season        2019            114.0   \n",
       "2  tt0069049       The Other Side of the Wind        2018            122.0   \n",
       "3  tt0069204                  Sabse Bada Sukh        2018              NaN   \n",
       "4  tt0100275         The Wandering Soap Opera        2017             80.0   \n",
       "\n",
       "                 genres  averagerating  numvotes  Action  Comedy  Drama  ...  \\\n",
       "0    Action,Crime,Drama            7.0        77    True   False   True  ...   \n",
       "1       Biography,Drama            7.2        43   False   False   True  ...   \n",
       "2                 Drama            6.9      4517   False   False   True  ...   \n",
       "3          Comedy,Drama            6.1        13   False    True   True  ...   \n",
       "4  Comedy,Drama,Fantasy            6.5       119   False    True   True  ...   \n",
       "\n",
       "   Thriller  Western  Sci-Fi  Animation  Documentary  Biography  Crime  \\\n",
       "0     False    False   False      False        False      False   True   \n",
       "1     False    False   False      False        False       True  False   \n",
       "2     False    False   False      False        False      False  False   \n",
       "3     False    False   False      False        False      False  False   \n",
       "4     False    False   False      False        False      False  False   \n",
       "\n",
       "   Musical  History  Family  \n",
       "0    False    False   False  \n",
       "1    False    False   False  \n",
       "2    False    False   False  \n",
       "3    False    False   False  \n",
       "4    False    False   False  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_basics_reviews_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create genre popularity dict with genre name as keys and number of movies with that genre as values. The total values of all\n",
    "#keys will be more than the total movies in the data because some movies are considered as multiple genres.\n",
    "\n",
    "genre_popularity = { genre: movie_basics_reviews_cleaned[genre].sum() for genre in genre_list}\n",
    "genre_popularity_values_sorted = dict(sorted(genre_popularity.items(),key=lambda item: item[1]))\n",
    "genre_popularity_keys_sorted = dict(sorted(genre_popularity.items(),key=lambda item: item[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Action': 6988,\n",
       " 'Comedy': 17290,\n",
       " 'Drama': 30788,\n",
       " 'Fantasy': 2126,\n",
       " 'Horror': 7674,\n",
       " 'Mystery': 3039,\n",
       " 'Romance': 6589,\n",
       " 'Thriller': 8217,\n",
       " 'Western': 280,\n",
       " 'Sci-Fi': 2206,\n",
       " 'Animation': 1743,\n",
       " 'Documentary': 17753,\n",
       " 'Biography': 3809,\n",
       " 'Crime': 4611,\n",
       " 'Musical': 721,\n",
       " 'History': 2825,\n",
       " 'Family': 3412}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7063.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7946.144128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>280.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2206.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3809.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7674.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30788.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "count     17.000000\n",
       "mean    7063.000000\n",
       "std     7946.144128\n",
       "min      280.000000\n",
       "25%     2206.000000\n",
       "50%     3809.000000\n",
       "75%     7674.000000\n",
       "max    30788.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(genre_popularity.values()).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplot of genre popularity. Shows the genres that are outliers in terms of popularity\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.boxplot(list(genre_popularity.values()))\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar Plot of Genre Popularity. \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.barh(list(genre_popularity_values_sorted.keys()),genre_popularity_values_sorted.values())\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create empty dictionary that we will populate with genres as keys and average rating of all movies in the genre as values\n",
    "genre_rating ={} \n",
    "\n",
    "#Calculating average rating:\n",
    "for genre in genre_list: #loop through genre in the genre list defined prior\n",
    "    n = 0 #number of movies \n",
    "    rating_sum = 0 #rating summation\n",
    "    idx =0  #keep track of row index\n",
    "    for row in movie_basics_reviews_cleaned[genre]: #go down row by row in genre column\n",
    "        if (row==True): #if genre in genre list is in genre in the row\n",
    "#             print(test['averagerating'][0])\n",
    "            rating_sum += movie_basics_reviews_cleaned['averagerating'][idx] #grab the average rating and add it to the total sum\n",
    "            n+=1 #add one to count of movie with that genre\n",
    "        idx +=1 #keep track of row index\n",
    "    genre_rating[genre] = rating_sum/n #calculate average rating of the genre\n",
    "    \n",
    "genre_rating_values_sorted =dict(sorted(genre_rating.items(),key=lambda item: item[1]))\n",
    "genre_rating_keys_sorted = dict(sorted(genre_rating.items(),key=lambda item: item[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Action': 5.810360618202613,\n",
       " 'Comedy': 6.002689415847302,\n",
       " 'Drama': 6.401559048980236,\n",
       " 'Fantasy': 5.919473189087487,\n",
       " 'Horror': 5.00344018764659,\n",
       " 'Mystery': 5.920401447844683,\n",
       " 'Romance': 6.146607983001951,\n",
       " 'Thriller': 5.639114031885117,\n",
       " 'Western': 5.868214285714282,\n",
       " 'Sci-Fi': 5.4897552130552985,\n",
       " 'Animation': 6.248307515777409,\n",
       " 'Documentary': 7.332090350926571,\n",
       " 'Biography': 7.162273562614873,\n",
       " 'Crime': 6.115441335935799,\n",
       " 'Musical': 6.498335644937577,\n",
       " 'History': 7.040955752212392,\n",
       " 'Family': 6.394724501758511}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average rating by genre\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.barh(list(genre_rating_values_sorted.keys()),genre_rating_values_sorted.values())\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looking at correlation between genre popularity and genre's average rating\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(genre_popularity_keys_sorted.values(),genre_rating_keys_sorted.values())\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looking at correlation between average rating and number of reviews\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(movie_basics_reviews_cleaned['numvotes'],movie_basics_reviews_cleaned['averagerating'])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Genre Popularity using TMDB data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Action',\n",
       " 'Adventure',\n",
       " 'Animation',\n",
       " 'Comedy',\n",
       " 'Crime',\n",
       " 'Documentary',\n",
       " 'Drama',\n",
       " 'Family',\n",
       " 'Fantasy',\n",
       " 'History',\n",
       " 'Horror',\n",
       " 'Music',\n",
       " 'Mystery',\n",
       " 'Romance',\n",
       " 'Science Fiction',\n",
       " 'TV Movie',\n",
       " 'Thriller',\n",
       " 'War',\n",
       " 'Western',\n",
       " 'Unknown']"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmdb_genre_list= list(tmdb_genre.values())\n",
    "tmdb_genre_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "for genre in tmdb_genre_list:\n",
    "    tmdb_movies_df[genre]=[True if genre in movie else False for movie in tmdb_movies_df['genre_names']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create empty dictionary that we will populate with studios as keys and average total worldwide gross\n",
    "#of all movies in the genre as values\n",
    "tmdb_genre_popularity ={} \n",
    "tmdb_genre_movie_counts = {}\n",
    "\n",
    "#Calculating average worldwide gross:\n",
    "for genre in tmdb_genre_list: #loop through studios in the studio list \n",
    "    n = 0 #number of movies \n",
    "    popularity_sum = 0 #rating summation\n",
    "    idx =0  #keep track of row index\n",
    "    for row in tmdb_movies_df[genre]: #go down row by row in studio column\n",
    "        if (row==True): #if genre in genre list is in genre in the row\n",
    "#             print(test['averagerating'][0])\n",
    "            popularity_sum += (tmdb_movies_df['popularity'][idx]) #grab the total gross and add it to the total sum\n",
    "            n+=1 #add one to count of movie with that studio production\n",
    "        idx +=1 #keep track of row index\n",
    "    tmdb_genre_popularity[genre] = popularity_sum/n #calculate average rating of the genre\n",
    "    tmdb_genre_movie_counts[genre] = n\n",
    "    \n",
    "tmdb_genre_popularity_values_sorted =dict(sorted(tmdb_genre_popularity.items(),key=lambda item: item[1]))\n",
    "tmdb_genre_movie_counts_values_sorted = dict(sorted(tmdb_genre_movie_counts.items(),key=lambda item: item[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmdb_genre_popularity_values_sorted = pd.DataFrame.from_dict(tmdb_genre_popularity_values_sorted, orient = 'index')\n",
    "tmdb_genre_movie_counts_values_sorted = pd.DataFrame.from_dict(tmdb_genre_movie_counts_values_sorted, orient = 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmdb_genre_popularity_values_sorted = tmdb_genre_popularity_values_sorted.sort_values(0,ascending = False)\n",
    "tmdb_genre_movie_counts_values_sorted = tmdb_genre_movie_counts_values_sorted.reindex(index=tmdb_genre_popularity_values_sorted.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create empty dictionary that we will populate with studios as keys and average total worldwide gross\n",
    "#of all movies in the genre as values\n",
    "tmdb_genre_rating ={} \n",
    "\n",
    "#Calculating average worldwide gross:\n",
    "for genre in tmdb_genre_list: #loop through studios in the studio list \n",
    "    n = 0 #number of movies \n",
    "    rating_sum = 0 #rating summation\n",
    "    idx =0  #keep track of row index\n",
    "    for row in tmdb_movies_df[genre]: #go down row by row in studio column\n",
    "        if (row==True): #if genre in genre list is in genre in the row\n",
    "#             print(test['averagerating'][0])\n",
    "            rating_sum += (tmdb_movies_df['vote_average'][idx]) #grab the total gross and add it to the total sum\n",
    "            n+=1 #add one to count of movie with that studio production\n",
    "        idx +=1 #keep track of row index\n",
    "    tmdb_genre_rating[genre] = rating_sum/n #calculate average rating of the genre\n",
    "    \n",
    "# studio_avg_gross_values_sorted =dict(sorted(studio_avg_gross.items(),key=lambda item: item[1]))\n",
    "# studio_avg_gross_keys_sorted = dict(sorted(studio_avg_gross.items(),key=lambda item: item[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmdb_genre_rating_sorted = pd.DataFrame.from_dict(tmdb_genre_rating, orient = 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmdb_genre_rating_sorted = tmdb_genre_rating_sorted.reindex(index=tmdb_genre_popularity_values_sorted.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Initialize the matplotlib figure\n",
    "fig, ax = plt.subplots(nrows=2,ncols=1,figsize=(20,6), sharex=True)\n",
    "\n",
    "# Plot the total crashes\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x=tmdb_genre_popularity_values_sorted.index, y=tmdb_genre_popularity_values_sorted[0],\n",
    "            label=\"Average Rating\", color=\"r\", ax =ax[1])\n",
    "ax[1].set_ylabel ('Popularity')\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(x=tmdb_genre_rating_sorted.index, y=tmdb_genre_rating_sorted[0],\n",
    "            label=\"Average Rating\", color=\"r\", ax =ax[0])\n",
    "ax[0].set_ylabel ('Average Rating')\n",
    "ax[1].set_xticklabels(ax[1].get_xticklabels(),rotation = 30)\n",
    "fig.suptitle(\"Genre Popularity\")\n",
    "plt.savefig('genre.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore how casts/directors/actors affects popularity and ROI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "principals_mb = principals_df.merge(movie_basics_df,how = 'left',on ='movie_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>ordering</th>\n",
       "      <th>person_id</th>\n",
       "      <th>category</th>\n",
       "      <th>job</th>\n",
       "      <th>characters</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>original_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>primary_name</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>death_year</th>\n",
       "      <th>primary_profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0111414</td>\n",
       "      <td>1</td>\n",
       "      <td>nm0246005</td>\n",
       "      <td>actor</td>\n",
       "      <td>None</td>\n",
       "      <td>[\"The Man\"]</td>\n",
       "      <td>A Thin Life</td>\n",
       "      <td>A Thin Life</td>\n",
       "      <td>2018</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Tommy Dysart</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>actor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0111414</td>\n",
       "      <td>2</td>\n",
       "      <td>nm0398271</td>\n",
       "      <td>director</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>A Thin Life</td>\n",
       "      <td>A Thin Life</td>\n",
       "      <td>2018</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Frank Howson</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>actor,writer,producer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0111414</td>\n",
       "      <td>3</td>\n",
       "      <td>nm3739909</td>\n",
       "      <td>producer</td>\n",
       "      <td>producer</td>\n",
       "      <td>None</td>\n",
       "      <td>A Thin Life</td>\n",
       "      <td>A Thin Life</td>\n",
       "      <td>2018</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Barry Porter-Robinson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer,art_department</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id  ordering  person_id  category       job   characters  \\\n",
       "0  tt0111414         1  nm0246005     actor      None  [\"The Man\"]   \n",
       "1  tt0111414         2  nm0398271  director      None         None   \n",
       "2  tt0111414         3  nm3739909  producer  producer         None   \n",
       "\n",
       "  primary_title original_title  start_year  runtime_minutes  genres  \\\n",
       "0   A Thin Life    A Thin Life        2018             75.0  Comedy   \n",
       "1   A Thin Life    A Thin Life        2018             75.0  Comedy   \n",
       "2   A Thin Life    A Thin Life        2018             75.0  Comedy   \n",
       "\n",
       "            primary_name  birth_year  death_year       primary_profession  \n",
       "0           Tommy Dysart         NaN         NaN                    actor  \n",
       "1           Frank Howson      1952.0         NaN    actor,writer,producer  \n",
       "2  Barry Porter-Robinson         NaN         NaN  producer,art_department  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principals_mb_persons = principals_mb.merge(persons_df,how ='left',on ='person_id')\n",
    "principals_mb_persons.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "principals_mb_persons = principals_mb_persons.drop(['characters','original_title','job','runtime_minutes','death_year','birth_year','primary_profession'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-f1f9ac6d6bb5>:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  ds[idx] = row.replace(char,\"\")\n"
     ]
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       "      <td>$30,080,225</td>\n",
       "      <td>64160680.0</td>\n",
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       "      <td>Hail, Caesar!</td>\n",
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       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>Ralph Fiennes</td>\n",
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      ],
      "text/plain": [
       "    movie_id  ordering  person_id         category  primary_title  start_year  \\\n",
       "0  tt0475290        10  nm0005683  cinematographer  Hail, Caesar!        2016   \n",
       "1  tt0475290         1  nm0000982            actor  Hail, Caesar!        2016   \n",
       "2  tt0475290         2  nm0000123            actor  Hail, Caesar!        2016   \n",
       "3  tt0475290         3  nm2403277            actor  Hail, Caesar!        2016   \n",
       "4  tt0475290         4  nm0000146            actor  Hail, Caesar!        2016   \n",
       "\n",
       "               genres      primary_name  id release_date          movie  \\\n",
       "0  Comedy,Drama,Music     Roger Deakins  22  Feb 5, 2016  Hail, Caesar!   \n",
       "1  Comedy,Drama,Music       Josh Brolin  22  Feb 5, 2016  Hail, Caesar!   \n",
       "2  Comedy,Drama,Music    George Clooney  22  Feb 5, 2016  Hail, Caesar!   \n",
       "3  Comedy,Drama,Music  Alden Ehrenreich  22  Feb 5, 2016  Hail, Caesar!   \n",
       "4  Comedy,Drama,Music     Ralph Fiennes  22  Feb 5, 2016  Hail, Caesar!   \n",
       "\n",
       "  production_budget domestic_gross  worldwide_gross  \n",
       "0       $22,000,000    $30,080,225       64160680.0  \n",
       "1       $22,000,000    $30,080,225       64160680.0  \n",
       "2       $22,000,000    $30,080,225       64160680.0  \n",
       "3       $22,000,000    $30,080,225       64160680.0  \n",
       "4       $22,000,000    $30,080,225       64160680.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person_gross_df = principals_mb_persons.merge(movie_budget_df, how = 'inner',left_on ='primary_title',right_on = 'movie')\n",
    "remove_chars(person_gross_df['worldwide_gross'],['$',',']);\n",
    "person_gross_df['worldwide_gross'] = person_gross_df['worldwide_gross'].astype(float)\n",
    "person_gross_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Comedy,Drama,Music</td>\n",
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       "      <td>Hail, Caesar!</td>\n",
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       "      <td>Ralph Fiennes</td>\n",
       "      <td>22</td>\n",
       "      <td>Feb 5, 2016</td>\n",
       "      <td>Hail, Caesar!</td>\n",
       "      <td>$22,000,000</td>\n",
       "      <td>$30,080,225</td>\n",
       "      <td>64160680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>tt10300704</td>\n",
       "      <td>1</td>\n",
       "      <td>nm0006763</td>\n",
       "      <td>actor</td>\n",
       "      <td>Walter</td>\n",
       "      <td>2019</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Jackie Shroff</td>\n",
       "      <td>96</td>\n",
       "      <td>Mar 13, 2015</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id  ordering  person_id category  primary_title  start_year  \\\n",
       "1    tt0475290         1  nm0000982    actor  Hail, Caesar!        2016   \n",
       "2    tt0475290         2  nm0000123    actor  Hail, Caesar!        2016   \n",
       "3    tt0475290         3  nm2403277    actor  Hail, Caesar!        2016   \n",
       "4    tt0475290         4  nm0000146    actor  Hail, Caesar!        2016   \n",
       "11  tt10300704         1  nm0006763    actor         Walter        2019   \n",
       "\n",
       "                genres      primary_name  id  release_date          movie  \\\n",
       "1   Comedy,Drama,Music       Josh Brolin  22   Feb 5, 2016  Hail, Caesar!   \n",
       "2   Comedy,Drama,Music    George Clooney  22   Feb 5, 2016  Hail, Caesar!   \n",
       "3   Comedy,Drama,Music  Alden Ehrenreich  22   Feb 5, 2016  Hail, Caesar!   \n",
       "4   Comedy,Drama,Music     Ralph Fiennes  22   Feb 5, 2016  Hail, Caesar!   \n",
       "11            Thriller     Jackie Shroff  96  Mar 13, 2015         Walter   \n",
       "\n",
       "   production_budget domestic_gross  worldwide_gross  \n",
       "1        $22,000,000    $30,080,225       64160680.0  \n",
       "2        $22,000,000    $30,080,225       64160680.0  \n",
       "3        $22,000,000    $30,080,225       64160680.0  \n",
       "4        $22,000,000    $30,080,225       64160680.0  \n",
       "11          $700,000             $0              0.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors_gross = person_gross_df[person_gross_df['category'] == \"actor\"]\n",
    "actors_gross.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id              0\n",
       "ordering              0\n",
       "person_id             0\n",
       "category              0\n",
       "primary_title         0\n",
       "start_year            0\n",
       "genres               92\n",
       "primary_name          1\n",
       "id                    0\n",
       "release_date          0\n",
       "movie                 0\n",
       "production_budget     0\n",
       "domestic_gross        0\n",
       "worldwide_gross       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors_gross.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Oct 17, 2014</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25514</th>\n",
       "      <td>tt2926198</td>\n",
       "      <td>4</td>\n",
       "      <td>nm9965423</td>\n",
       "      <td>actor</td>\n",
       "      <td>Enough</td>\n",
       "      <td>2013</td>\n",
       "      <td>Drama,History</td>\n",
       "      <td>Benjamin Potgieter</td>\n",
       "      <td>68</td>\n",
       "      <td>May 24, 2002</td>\n",
       "      <td>Enough</td>\n",
       "      <td>$38,000,000</td>\n",
       "      <td>$39,177,215</td>\n",
       "      <td>50970660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4688</th>\n",
       "      <td>tt8692378</td>\n",
       "      <td>3</td>\n",
       "      <td>nm9968164</td>\n",
       "      <td>actor</td>\n",
       "      <td>After</td>\n",
       "      <td>2018</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Rene Little</td>\n",
       "      <td>2</td>\n",
       "      <td>Dec 31, 2012</td>\n",
       "      <td>After</td>\n",
       "      <td>$650,000</td>\n",
       "      <td>$0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4687</th>\n",
       "      <td>tt8692378</td>\n",
       "      <td>2</td>\n",
       "      <td>nm9968166</td>\n",
       "      <td>actor</td>\n",
       "      <td>After</td>\n",
       "      <td>2018</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Audrey Lingle</td>\n",
       "      <td>2</td>\n",
       "      <td>Dec 31, 2012</td>\n",
       "      <td>After</td>\n",
       "      <td>$650,000</td>\n",
       "      <td>$0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4689</th>\n",
       "      <td>tt8692378</td>\n",
       "      <td>4</td>\n",
       "      <td>nm9968169</td>\n",
       "      <td>actor</td>\n",
       "      <td>After</td>\n",
       "      <td>2018</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Kiko Medrano IV</td>\n",
       "      <td>2</td>\n",
       "      <td>Dec 31, 2012</td>\n",
       "      <td>After</td>\n",
       "      <td>$650,000</td>\n",
       "      <td>$0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>tt8692378</td>\n",
       "      <td>1</td>\n",
       "      <td>nm9968170</td>\n",
       "      <td>actor</td>\n",
       "      <td>After</td>\n",
       "      <td>2018</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Leila Finn</td>\n",
       "      <td>2</td>\n",
       "      <td>Dec 31, 2012</td>\n",
       "      <td>After</td>\n",
       "      <td>$650,000</td>\n",
       "      <td>$0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8423 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        movie_id  ordering  person_id category        primary_title  \\\n",
       "19014  tt1053810         4  nm0000092    actor         The Big Year   \n",
       "10620  tt1449283         3  nm0000092    actor      Winnie the Pooh   \n",
       "25598  tt2713180         1  nm0000093    actor                 Fury   \n",
       "14551  tt1596363         4  nm0000093    actor        The Big Short   \n",
       "2283   tt1764234         1  nm0000093    actor  Killing Them Softly   \n",
       "...          ...       ...        ...      ...                  ...   \n",
       "25514  tt2926198         4  nm9965423    actor               Enough   \n",
       "4688   tt8692378         3  nm9968164    actor                After   \n",
       "4687   tt8692378         2  nm9968166    actor                After   \n",
       "4689   tt8692378         4  nm9968169    actor                After   \n",
       "4686   tt8692378         1  nm9968170    actor                After   \n",
       "\n",
       "       start_year                      genres        primary_name  id  \\\n",
       "19014        2011                      Comedy         John Cleese  74   \n",
       "10620        2011  Adventure,Animation,Comedy         John Cleese  39   \n",
       "25598        2014            Action,Drama,War           Brad Pitt  68   \n",
       "14551        2015      Biography,Comedy,Drama           Brad Pitt  42   \n",
       "2283         2012              Crime,Thriller           Brad Pitt  82   \n",
       "...           ...                         ...                 ...  ..   \n",
       "25514        2013               Drama,History  Benjamin Potgieter  68   \n",
       "4688         2018                   Adventure         Rene Little   2   \n",
       "4687         2018                   Adventure       Audrey Lingle   2   \n",
       "4689         2018                   Adventure     Kiko Medrano IV   2   \n",
       "4686         2018                   Adventure          Leila Finn   2   \n",
       "\n",
       "       release_date                movie production_budget domestic_gross  \\\n",
       "19014  Oct 14, 2011         The Big Year       $41,000,000     $7,204,138   \n",
       "10620  Jul 15, 2011      Winnie the Pooh       $30,000,000    $26,692,846   \n",
       "25598  Oct 17, 2014                 Fury       $80,000,000    $85,817,906   \n",
       "14551  Dec 11, 2015        The Big Short       $28,000,000    $70,259,870   \n",
       "2283   Nov 30, 2012  Killing Them Softly       $15,000,000    $14,945,541   \n",
       "...             ...                  ...               ...            ...   \n",
       "25514  May 24, 2002               Enough       $38,000,000    $39,177,215   \n",
       "4688   Dec 31, 2012                After          $650,000             $0   \n",
       "4687   Dec 31, 2012                After          $650,000             $0   \n",
       "4689   Dec 31, 2012                After          $650,000             $0   \n",
       "4686   Dec 31, 2012                After          $650,000             $0   \n",
       "\n",
       "       worldwide_gross  \n",
       "19014        7684524.0  \n",
       "10620       50145607.0  \n",
       "25598      210315681.0  \n",
       "14551      133162752.0  \n",
       "2283        39238443.0  \n",
       "...                ...  \n",
       "25514       50970660.0  \n",
       "4688               0.0  \n",
       "4687               0.0  \n",
       "4689               0.0  \n",
       "4686               0.0  \n",
       "\n",
       "[8423 rows x 14 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(actors_gross['person_id'].unique())\n",
    "actors_gross.sort_values(by = 'person_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dwayne Johnson          19\n",
       "Mark Wahlberg           19\n",
       "Robert De Niro          18\n",
       "Liam Neeson             18\n",
       "Channing Tatum          17\n",
       "                        ..\n",
       "Andrius Bialobzeskis     1\n",
       "Reid Warner              1\n",
       "Ben Chaplin              1\n",
       "JM de Guzman             1\n",
       "Seth Hendricks           1\n",
       "Name: primary_name, Length: 5516, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors_gross['primary_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "actors_10 = list(actors_gross['primary_name'].value_counts()[actors_gross['primary_name'].value_counts() > 10].index)\n",
    "# actors starring in more than 10 movies.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 619 entries, 0 to 618\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   movie_id           619 non-null    object \n",
      " 1   ordering           619 non-null    int64  \n",
      " 2   person_id          619 non-null    object \n",
      " 3   category           619 non-null    object \n",
      " 4   primary_title      619 non-null    object \n",
      " 5   start_year         619 non-null    int64  \n",
      " 6   genres             619 non-null    object \n",
      " 7   primary_name       619 non-null    object \n",
      " 8   id                 619 non-null    int64  \n",
      " 9   release_date       619 non-null    object \n",
      " 10  movie              619 non-null    object \n",
      " 11  production_budget  619 non-null    object \n",
      " 12  domestic_gross     619 non-null    object \n",
      " 13  worldwide_gross    619 non-null    float64\n",
      "dtypes: float64(1), int64(3), object(10)\n",
      "memory usage: 67.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#filter out actors who starred less than 10 movies\n",
    "\n",
    "actors_10_df= actors_gross.groupby(\"primary_name\").filter(lambda x: len(x) > 10) \n",
    "actors_10_df = actors_10_df.reset_index(drop = True)\n",
    "actors_10_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create empty dictionary that we will populate with studios as keys and average total worldwide gross\n",
    "#of all movies in the genre as values\n",
    "actor_value_by_gross ={} \n",
    "\n",
    "#Calculating average worldwide gross:\n",
    "for actor in actors_10: #loop through studios in the studio list \n",
    "    n = 0 #number of movies \n",
    "    gross_sum = 0 #rating summation\n",
    "    idx =0  #keep track of row index\n",
    "    for row in actors_10_df['primary_name']: #go down row by row in studio column\n",
    "        if (row==actor): #if genre in genre list is in genre in the row\n",
    "#             print(test['averagerating'][0])\n",
    "            gross_sum += actors_10_df['worldwide_gross'][idx] #grab the total gross and add it to the total sum\n",
    "            n+=1 #add one to count of movie with that studio production\n",
    "        idx +=1 #keep track of row index\n",
    "    actor_value_by_gross[actor] = gross_sum/n #calculate average rating of the genre\n",
    "    \n",
    "# studio_avg_gross_values_sorted =dict(sorted(studio_avg_gross.items(),key=lambda item: item[1]))\n",
    "# studio_avg_gross_keys_sorted = dict(sorted(studio_avg_gross.items(),key=lambda item: item[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Robert Downey Jr.</th>\n",
       "      <td>8.816342e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris Evans</th>\n",
       "      <td>5.716831e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dwayne Johnson</th>\n",
       "      <td>4.233051e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris Hemsworth</th>\n",
       "      <td>4.137017e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samuel L. Jackson</th>\n",
       "      <td>3.314483e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnny Depp</th>\n",
       "      <td>3.220359e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jason Statham</th>\n",
       "      <td>3.209206e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ryan Reynolds</th>\n",
       "      <td>2.556848e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woody Harrelson</th>\n",
       "      <td>2.533043e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Hardy</th>\n",
       "      <td>2.502626e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bradley Cooper</th>\n",
       "      <td>2.465359e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris Pine</th>\n",
       "      <td>2.452602e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Hanks</th>\n",
       "      <td>2.226729e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gary Oldman</th>\n",
       "      <td>2.211481e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James McAvoy</th>\n",
       "      <td>2.096196e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve Carell</th>\n",
       "      <td>2.067805e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin Hart</th>\n",
       "      <td>2.042078e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mark Wahlberg</th>\n",
       "      <td>2.016023e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matt Damon</th>\n",
       "      <td>1.971410e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jude Law</th>\n",
       "      <td>1.910015e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jason Bateman</th>\n",
       "      <td>1.781000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owen Wilson</th>\n",
       "      <td>1.732019e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gerard Butler</th>\n",
       "      <td>1.724421e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liam Neeson</th>\n",
       "      <td>1.660747e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channing Tatum</th>\n",
       "      <td>1.648536e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonah Hill</th>\n",
       "      <td>1.629256e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seth Rogen</th>\n",
       "      <td>1.609989e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Josh Brolin</th>\n",
       "      <td>1.587145e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brad Pitt</th>\n",
       "      <td>1.557419e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthew McConaughey</th>\n",
       "      <td>1.469527e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joseph Gordon-Levitt</th>\n",
       "      <td>1.439698e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael Fassbender</th>\n",
       "      <td>1.421535e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Will Ferrell</th>\n",
       "      <td>1.228431e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Goodman</th>\n",
       "      <td>1.208429e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colin Farrell</th>\n",
       "      <td>1.200931e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mark Strong</th>\n",
       "      <td>1.168828e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zac Efron</th>\n",
       "      <td>1.114322e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ryan Gosling</th>\n",
       "      <td>1.096510e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Franco</th>\n",
       "      <td>1.053698e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bruce Willis</th>\n",
       "      <td>9.458397e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robert De Niro</th>\n",
       "      <td>8.652598e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael Shannon</th>\n",
       "      <td>8.548369e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicolas Cage</th>\n",
       "      <td>8.152494e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joel Edgerton</th>\n",
       "      <td>7.576603e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miles Teller</th>\n",
       "      <td>6.676263e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jake Gyllenhaal</th>\n",
       "      <td>6.224437e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 0\n",
       "Robert Downey Jr.     8.816342e+08\n",
       "Chris Evans           5.716831e+08\n",
       "Dwayne Johnson        4.233051e+08\n",
       "Chris Hemsworth       4.137017e+08\n",
       "Samuel L. Jackson     3.314483e+08\n",
       "Johnny Depp           3.220359e+08\n",
       "Jason Statham         3.209206e+08\n",
       "Ryan Reynolds         2.556848e+08\n",
       "Woody Harrelson       2.533043e+08\n",
       "Tom Hardy             2.502626e+08\n",
       "Bradley Cooper        2.465359e+08\n",
       "Chris Pine            2.452602e+08\n",
       "Tom Hanks             2.226729e+08\n",
       "Gary Oldman           2.211481e+08\n",
       "James McAvoy          2.096196e+08\n",
       "Steve Carell          2.067805e+08\n",
       "Kevin Hart            2.042078e+08\n",
       "Mark Wahlberg         2.016023e+08\n",
       "Matt Damon            1.971410e+08\n",
       "Jude Law              1.910015e+08\n",
       "Jason Bateman         1.781000e+08\n",
       "Owen Wilson           1.732019e+08\n",
       "Gerard Butler         1.724421e+08\n",
       "Liam Neeson           1.660747e+08\n",
       "Channing Tatum        1.648536e+08\n",
       "Jonah Hill            1.629256e+08\n",
       "Seth Rogen            1.609989e+08\n",
       "Josh Brolin           1.587145e+08\n",
       "Brad Pitt             1.557419e+08\n",
       "Matthew McConaughey   1.469527e+08\n",
       "Joseph Gordon-Levitt  1.439698e+08\n",
       "Michael Fassbender    1.421535e+08\n",
       "Will Ferrell          1.228431e+08\n",
       "John Goodman          1.208429e+08\n",
       "Colin Farrell         1.200931e+08\n",
       "Mark Strong           1.168828e+08\n",
       "Zac Efron             1.114322e+08\n",
       "Ryan Gosling          1.096510e+08\n",
       "James Franco          1.053698e+08\n",
       "Bruce Willis          9.458397e+07\n",
       "Robert De Niro        8.652598e+07\n",
       "Michael Shannon       8.548369e+07\n",
       "Nicolas Cage          8.152494e+07\n",
       "Joel Edgerton         7.576603e+07\n",
       "Miles Teller          6.676263e+07\n",
       "Jake Gyllenhaal       6.224437e+07"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actor_value_by_gross_df = pd.DataFrame.from_dict(actor_value_by_gross, orient = 'index')\n",
    "actor_value_by_gross_df.sort_values(by = 0, ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 20 Studios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rotten Tomatoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "rt_movies_df_cleaned = rt_movies_df.dropna(subset = ['production_company','audience_rating'])\n",
    "rt_movies_df_cleaned = rt_movies_df_cleaned.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16972 entries, 0 to 16971\n",
      "Data columns (total 22 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   rotten_tomatoes_link              16972 non-null  object \n",
      " 1   movie_title                       16972 non-null  object \n",
      " 2   movie_info                        16767 non-null  object \n",
      " 3   critics_consensus                 9020 non-null   object \n",
      " 4   content_rating                    16972 non-null  object \n",
      " 5   genres                            16957 non-null  object \n",
      " 6   directors                         16823 non-null  object \n",
      " 7   authors                           15665 non-null  object \n",
      " 8   actors                            16695 non-null  object \n",
      " 9   original_release_date             16069 non-null  object \n",
      " 10  streaming_release_date            16696 non-null  object \n",
      " 11  runtime                           16762 non-null  float64\n",
      " 12  production_company                16972 non-null  object \n",
      " 13  tomatometer_status                16964 non-null  object \n",
      " 14  tomatometer_rating                16964 non-null  float64\n",
      " 15  tomatometer_count                 16964 non-null  float64\n",
      " 16  audience_status                   16833 non-null  object \n",
      " 17  audience_rating                   16972 non-null  float64\n",
      " 18  audience_count                    16971 non-null  float64\n",
      " 19  tomatometer_top_critics_count     16972 non-null  int64  \n",
      " 20  tomatometer_fresh_critics_count   16972 non-null  int64  \n",
      " 21  tomatometer_rotten_critics_count  16972 non-null  int64  \n",
      "dtypes: float64(5), int64(3), object(14)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "rt_movies_df_cleaned.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "studio_dict={\"Sony\": \"Sony Pictures Entertainment\", \"Warner\":\"Warner Brothers\",\"Fox\":\"21st Century Fox\"\n",
    "             , \"Paramount\":\"Paramount Pictures\",'MGM':'MGM', 'HBO':\"HBO\",\"Lion\":\"Lions Gate\",\"Miramax\":\"Miramax\",\n",
    "             \"Disney\": \"Walt Disney\",\"WARNER\":\"Warner Brothers\", \"Buena\":\"Buena Vista\",\"MCA\":\"Universal Pictures\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2981"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rt_movies_df_cleaned['production_company'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16972, 22)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt_movies_df_cleaned.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-65-a7adf304c4b5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rt_movies_df_cleaned['production_company'][idx] = value\n"
     ]
    }
   ],
   "source": [
    "idx = 0\n",
    "for company in rt_movies_df_cleaned['production_company']:\n",
    "    for key,value in studio_dict.items():\n",
    "        if key in company:\n",
    "#             print (company, value)\n",
    "#             print (\"Before: \", rt_movies_df_cleaned['production_company'][idx])\n",
    "            rt_movies_df_cleaned['production_company'][idx] = value\n",
    "#             print(\"After: \", rt_movies_df_cleaned['production_company'][idx])\n",
    "    idx +=1     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2756"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rt_movies_df_cleaned['production_company'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16972, 22)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt_movies_df_cleaned.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Warner Brothers                1254\n",
       "Sony Pictures Entertainment    1073\n",
       "21st Century Fox                905\n",
       "Paramount Pictures              808\n",
       "Universal Pictures              665\n",
       "MGM                             598\n",
       "Lions Gate                      454\n",
       "IFC Films                       413\n",
       "Miramax                         329\n",
       "Netflix                         329\n",
       "Walt Disney                     260\n",
       "Magnolia Pictures               254\n",
       "Columbia Pictures               237\n",
       "Buena Vista                     236\n",
       "United Artists                  168\n",
       "New Line Cinema                 147\n",
       "Focus Features                  133\n",
       "HBO                             132\n",
       "Criterion Collection            112\n",
       "Gravitas Ventures               111\n",
       "Name: production_company, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_20_studios = list(rt_movies_df_cleaned['production_company'].value_counts().index)[0:20]\n",
    "top_20_studios_counts = rt_movies_df_cleaned['production_company'].value_counts()[0:20]\n",
    "top_20_studios_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rotten_tomatoes_link</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>movie_info</th>\n",
       "      <th>critics_consensus</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genres</th>\n",
       "      <th>directors</th>\n",
       "      <th>authors</th>\n",
       "      <th>actors</th>\n",
       "      <th>original_release_date</th>\n",
       "      <th>...</th>\n",
       "      <th>production_company</th>\n",
       "      <th>tomatometer_status</th>\n",
       "      <th>tomatometer_rating</th>\n",
       "      <th>tomatometer_count</th>\n",
       "      <th>audience_status</th>\n",
       "      <th>audience_rating</th>\n",
       "      <th>audience_count</th>\n",
       "      <th>tomatometer_top_critics_count</th>\n",
       "      <th>tomatometer_fresh_critics_count</th>\n",
       "      <th>tomatometer_rotten_critics_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16969</th>\n",
       "      <td>m/zorba_the_greek</td>\n",
       "      <td>Zorba the Greek</td>\n",
       "      <td>Traveling to inspect an abandoned mine his fat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>Action &amp; Adventure, Art House &amp; International,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Anthony Quinn, Alan Bates, Irene Papas, Lila K...</td>\n",
       "      <td>1964-12-17</td>\n",
       "      <td>...</td>\n",
       "      <td>21st Century Fox</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Upright</td>\n",
       "      <td>86.0</td>\n",
       "      <td>7146.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>m/zulu</td>\n",
       "      <td>Zulu</td>\n",
       "      <td>In 1879, the Zulu nation hands colonial Britis...</td>\n",
       "      <td>Zulu patiently establishes a cast of colorful ...</td>\n",
       "      <td>PG</td>\n",
       "      <td>Classics, Drama</td>\n",
       "      <td>Cy Endfield, Cyril Endfield</td>\n",
       "      <td>Cy Endfield, John Prebble</td>\n",
       "      <td>Stanley Baker, Jack Hawkins, Ulla Jacobsson, J...</td>\n",
       "      <td>1964-06-17</td>\n",
       "      <td>...</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>96.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Upright</td>\n",
       "      <td>91.0</td>\n",
       "      <td>30193.0</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rotten_tomatoes_link      movie_title  \\\n",
       "16969    m/zorba_the_greek  Zorba the Greek   \n",
       "16970               m/zulu             Zulu   \n",
       "\n",
       "                                              movie_info  \\\n",
       "16969  Traveling to inspect an abandoned mine his fat...   \n",
       "16970  In 1879, the Zulu nation hands colonial Britis...   \n",
       "\n",
       "                                       critics_consensus content_rating  \\\n",
       "16969                                                NaN             NR   \n",
       "16970  Zulu patiently establishes a cast of colorful ...             PG   \n",
       "\n",
       "                                                  genres  \\\n",
       "16969  Action & Adventure, Art House & International,...   \n",
       "16970                                    Classics, Drama   \n",
       "\n",
       "                         directors                    authors  \\\n",
       "16969                          NaN                        NaN   \n",
       "16970  Cy Endfield, Cyril Endfield  Cy Endfield, John Prebble   \n",
       "\n",
       "                                                  actors  \\\n",
       "16969  Anthony Quinn, Alan Bates, Irene Papas, Lila K...   \n",
       "16970  Stanley Baker, Jack Hawkins, Ulla Jacobsson, J...   \n",
       "\n",
       "      original_release_date  ...  production_company  tomatometer_status  \\\n",
       "16969            1964-12-17  ...    21st Century Fox               Fresh   \n",
       "16970            1964-06-17  ...  Paramount Pictures               Fresh   \n",
       "\n",
       "      tomatometer_rating tomatometer_count  audience_status  audience_rating  \\\n",
       "16969               80.0              10.0          Upright             86.0   \n",
       "16970               96.0              23.0          Upright             91.0   \n",
       "\n",
       "      audience_count  tomatometer_top_critics_count  \\\n",
       "16969         7146.0                              0   \n",
       "16970        30193.0                              6   \n",
       "\n",
       "       tomatometer_fresh_critics_count  tomatometer_rotten_critics_count  \n",
       "16969                                8                                 2  \n",
       "16970                               22                                 1  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter dataframe to just movies produced by the top 20 studios\n",
    "rt_movies_top_20_studios = rt_movies_df_cleaned[rt_movies_df_cleaned['production_company'].isin(top_20_studios)]\n",
    "rt_movies_top_20_studios.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Warner Brothers                62.213716\n",
       "Sony Pictures Entertainment    61.731594\n",
       "21st Century Fox               62.423204\n",
       "Paramount Pictures             61.902228\n",
       "Universal Pictures             61.218045\n",
       "MGM                            63.198997\n",
       "Lions Gate                     57.200441\n",
       "IFC Films                      55.489104\n",
       "Miramax                        64.510638\n",
       "Netflix                        60.501520\n",
       "Walt Disney                    65.003846\n",
       "Magnolia Pictures              58.322835\n",
       "Columbia Pictures              63.092827\n",
       "Buena Vista                    62.072034\n",
       "United Artists                 68.434524\n",
       "New Line Cinema                62.530612\n",
       "Focus Features                 64.082707\n",
       "HBO                            64.333333\n",
       "Criterion Collection           84.000000\n",
       "Gravitas Ventures              59.747748\n",
       "Name: audience_rating, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_rating_by_studio = rt_movies_top_20_studios.groupby('production_company').mean()['audience_rating']\n",
    "average_rating_by_studio = average_rating_by_studio.reindex(index=top_20_studios_counts.index)\n",
    "# average_rating_by_studio = average_rating_by_studio.sort_values(ascending = False)\n",
    "average_rating_by_studio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BOM data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "#cleaning up foreign gross film\n",
    "movie_gross_df['foreign_gross'] = movie_gross_df['foreign_gross'].map(lambda x: 0 if x is np.nan else x)\n",
    "movie_gross_df['foreign_gross'] = movie_gross_df['foreign_gross'].str.replace(\",\",'')\n",
    "movie_gross_df['foreign_gross'] = movie_gross_df['foreign_gross'].astype(float)\n",
    "movie_gross_df['foreign_gross'] = movie_gross_df['foreign_gross'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleaning up domestic gross\n",
    "movie_gross_df['domestic_gross'] = movie_gross_df['domestic_gross'].fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3387 entries, 0 to 3386\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   title           3387 non-null   object \n",
      " 1   studio          3382 non-null   object \n",
      " 2   domestic_gross  3387 non-null   float64\n",
      " 3   foreign_gross   3387 non-null   float64\n",
      " 4   year            3387 non-null   int64  \n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 132.4+ KB\n"
     ]
    }
   ],
   "source": [
    "movie_gross_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_gross_df['total_gross'] = movie_gross_df['foreign_gross'] + movie_gross_df['domestic_gross']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "      <th>total_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>Magic Trip</td>\n",
       "      <td>Magn.</td>\n",
       "      <td>155000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>155000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>Act of Valor</td>\n",
       "      <td>Rela.</td>\n",
       "      <td>70000000.0</td>\n",
       "      <td>11300000.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>81300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>Tere Naal Love Ho Gaya</td>\n",
       "      <td>UTV</td>\n",
       "      <td>178000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>178000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2258</th>\n",
       "      <td>Out 1, noli me tangere</td>\n",
       "      <td>CFilms</td>\n",
       "      <td>31500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>31500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>20th Century Women</td>\n",
       "      <td>A24</td>\n",
       "      <td>5700000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>5700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>Dreams of a Life</td>\n",
       "      <td>Strand</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>8400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>Pain and Gain</td>\n",
       "      <td>Par.</td>\n",
       "      <td>49900000.0</td>\n",
       "      <td>36300000.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>86200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>Knuckle</td>\n",
       "      <td>Vita.</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>God Bless America</td>\n",
       "      <td>Magn.</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>123000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2888</th>\n",
       "      <td>Only The Brave</td>\n",
       "      <td>Sony</td>\n",
       "      <td>18300000.0</td>\n",
       "      <td>7400000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>25700000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       title  studio  domestic_gross  foreign_gross  year  \\\n",
       "668               Magic Trip   Magn.        155000.0            0.0  2011   \n",
       "806             Act of Valor   Rela.      70000000.0     11300000.0  2012   \n",
       "1044  Tere Naal Love Ho Gaya     UTV        178000.0            0.0  2012   \n",
       "2258  Out 1, noli me tangere  CFilms         31500.0            0.0  2015   \n",
       "2516      20th Century Women     A24       5700000.0            0.0  2016   \n",
       "1115        Dreams of a Life  Strand          8400.0            0.0  2012   \n",
       "1206           Pain and Gain    Par.      49900000.0     36300000.0  2013   \n",
       "723                  Knuckle   Vita.          2600.0            0.0  2011   \n",
       "1061       God Bless America   Magn.        123000.0            0.0  2012   \n",
       "2888          Only The Brave    Sony      18300000.0      7400000.0  2017   \n",
       "\n",
       "      total_gross  \n",
       "668      155000.0  \n",
       "806    81300000.0  \n",
       "1044     178000.0  \n",
       "2258      31500.0  \n",
       "2516    5700000.0  \n",
       "1115       8400.0  \n",
       "1206   86200000.0  \n",
       "723        2600.0  \n",
       "1061     123000.0  \n",
       "2888   25700000.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_gross_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "studio_counts = movie_gross_df['studio'].value_counts()[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#How many movies each studio make\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.barh(list(studio_counts.index), list(studio_counts.values))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create empty dictionary that we will populate with studios as keys and average total worldwide gross\n",
    "#of all movies in the genre as values\n",
    "studio_avg_gross ={} \n",
    "\n",
    "#Calculating average worldwide gross:\n",
    "for studio in list(studio_counts.index): #loop through studios in the studio list \n",
    "    n = 0 #number of movies \n",
    "    gross_sum = 0 #rating summation\n",
    "    idx =0  #keep track of row index\n",
    "    for row in movie_gross_df['studio']: #go down row by row in studio column\n",
    "        if (row==studio): #if genre in genre list is in genre in the row\n",
    "#             print(test['averagerating'][0])\n",
    "            gross_sum += movie_gross_df['total_gross'][idx] #grab the total gross and add it to the total sum\n",
    "            n+=1 #add one to count of movie with that studio production\n",
    "        idx +=1 #keep track of row index\n",
    "    studio_avg_gross[studio] = gross_sum/n #calculate average rating of the genre\n",
    "    \n",
    "studio_avg_gross_values_sorted =dict(sorted(studio_avg_gross.items(),key=lambda item: item[1]))\n",
    "studio_avg_gross_keys_sorted = dict(sorted(studio_avg_gross.items(),key=lambda item: item[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Warner Brothers',\n",
       " 'Sony Pictures Entertainment',\n",
       " '21st Century Fox',\n",
       " 'Paramount Pictures',\n",
       " 'Universal Pictures',\n",
       " 'MGM',\n",
       " 'Lions Gate',\n",
       " 'IFC Films',\n",
       " 'Miramax',\n",
       " 'Netflix',\n",
       " 'Walt Disney',\n",
       " 'Magnolia Pictures',\n",
       " 'Columbia Pictures',\n",
       " 'Buena Vista',\n",
       " 'United Artists',\n",
       " 'New Line Cinema',\n",
       " 'Focus Features',\n",
       " 'HBO',\n",
       " 'Criterion Collection',\n",
       " 'Gravitas Ventures']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_20_studios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "studio_avg_gross_top20 = {'IFC Films': 53098798,\n",
    " 'Universal Pictures': 46393858648,\n",
    " 'Warner Brothers': 47159264939,\n",
    " 'Magnolia Pictures': 66849364,\n",
    " '21st Century Fox': 26108756713,\n",
    " 'Sony Pictures Entertainment': 1059000000,\n",
    " 'Buena Vista': 417102678.29339623,\n",
    " 'Lions Gate': 10301277228,\n",
    " 'Paramount Pictures': 33408875884,\n",
    " 'Focus Features': 3213869603,\n",
    " 'MGM': 9680000000,\n",
    " 'Netflix':25000000000,\n",
    " 'Miramax':2367891269,\n",
    " 'Walt Disney':39781985733,\n",
    " 'Columbia Pictures':45184498666,\n",
    " 'United Artists':160772007,\n",
    " 'New Line Cinema':16836136035,\n",
    " 'HBO':7700000000,\n",
    " 'Criterion Collection':0, \"Gravitas Ventures\":8675574}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "studio_avg_gross_top20_ds = pd.DataFrame.from_dict(studio_avg_gross_top20, orient = \"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "studio_avg_gross_top20_ds = studio_avg_gross_top20_ds.sort_values(by = 0,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_20_studios_counts = top_20_studios_counts.reindex(index=studio_avg_gross_top20_ds.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Warner Brothers</th>\n",
       "      <td>47159.264939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Universal Pictures</th>\n",
       "      <td>46393.858648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columbia Pictures</th>\n",
       "      <td>45184.498666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walt Disney</th>\n",
       "      <td>39781.985733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paramount Pictures</th>\n",
       "      <td>33408.875884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21st Century Fox</th>\n",
       "      <td>26108.756713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netflix</th>\n",
       "      <td>25000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Line Cinema</th>\n",
       "      <td>16836.136035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lions Gate</th>\n",
       "      <td>10301.277228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MGM</th>\n",
       "      <td>9680.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HBO</th>\n",
       "      <td>7700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Focus Features</th>\n",
       "      <td>3213.869603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miramax</th>\n",
       "      <td>2367.891269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sony Pictures Entertainment</th>\n",
       "      <td>1059.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buena Vista</th>\n",
       "      <td>417.102678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Artists</th>\n",
       "      <td>160.772007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magnolia Pictures</th>\n",
       "      <td>66.849364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IFC Films</th>\n",
       "      <td>53.098798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gravitas Ventures</th>\n",
       "      <td>8.675574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Criterion Collection</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        0\n",
       "Warner Brothers              47159.264939\n",
       "Universal Pictures           46393.858648\n",
       "Columbia Pictures            45184.498666\n",
       "Walt Disney                  39781.985733\n",
       "Paramount Pictures           33408.875884\n",
       "21st Century Fox             26108.756713\n",
       "Netflix                      25000.000000\n",
       "New Line Cinema              16836.136035\n",
       "Lions Gate                   10301.277228\n",
       "MGM                           9680.000000\n",
       "HBO                           7700.000000\n",
       "Focus Features                3213.869603\n",
       "Miramax                       2367.891269\n",
       "Sony Pictures Entertainment   1059.000000\n",
       "Buena Vista                    417.102678\n",
       "United Artists                 160.772007\n",
       "Magnolia Pictures               66.849364\n",
       "IFC Films                       53.098798\n",
       "Gravitas Ventures                8.675574\n",
       "Criterion Collection             0.000000"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "studio_avg_gross_top20_ds = studio_avg_gross_top20_ds/1000000\n",
    "studio_avg_gross_top20_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Warner Brothers                1254\n",
       "Universal Pictures              665\n",
       "Columbia Pictures               237\n",
       "Walt Disney                     260\n",
       "Paramount Pictures              808\n",
       "21st Century Fox                905\n",
       "Netflix                         329\n",
       "New Line Cinema                 147\n",
       "Lions Gate                      454\n",
       "MGM                             598\n",
       "HBO                             132\n",
       "Focus Features                  133\n",
       "Miramax                         329\n",
       "Sony Pictures Entertainment    1073\n",
       "Buena Vista                     236\n",
       "United Artists                  168\n",
       "Magnolia Pictures               254\n",
       "IFC Films                       413\n",
       "Gravitas Ventures               111\n",
       "Criterion Collection            112\n",
       "Name: production_company, dtype: int64"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_20_studios_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Initialize the matplotlib figure\n",
    "fig, ax = plt.subplots(nrows=2,ncols=1,figsize=(20,6), sharex=True)\n",
    "\n",
    "# Plot the total crashes\n",
    "# sns.set_color_codes(\"pastel\")\n",
    "# sns.barplot(x=average_rating_by_studio.index, y=average_rating_by_studio.values,\n",
    "#             label=\"Average Rating\", color=\"b\", ax =ax[0])\n",
    "# ax[0].set_ylabel ('Average rating')\n",
    "\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(x=top_20_studios_counts.index, y=top_20_studios_counts.values, ax=ax[0],\n",
    "            label=\"Number of movies produced\", color=\"b\")\n",
    "ax[0].set_ylabel('Movies Count')\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x=studio_avg_gross_top20_ds.index, y=studio_avg_gross_top20_ds[0], ax=ax[1],\n",
    "            label=\"Number of movies produced\", color=\"b\")\n",
    "ax[1].set_ylabel(\"Worldwide Gross\")\n",
    "fig.suptitle (\"Production Studio Value\")\n",
    "\n",
    "ax[1].set_xticklabels(ax[1].get_xticklabels(),rotation = 30)\n",
    "\n",
    "# Add a legend and informative axis label\n",
    "# ax.legend(ncol=1, loc=\"lower right\", frameon=True)\n",
    "# ax.set(xlim=(0,100), ylabel=\"\",\n",
    "#        xlabel=\"Automobile collisions per billion miles\")\n",
    "# sns.despine(left=True, bottom=True)\n",
    "plt.savefig('studio_value.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (learn-env)",
   "language": "python",
   "name": "learn-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}